Certain terms and concepts may not exist in other languages, or have additional connotations that back translations do not always reveal. Challenges arise because of not only the content of word-for-word, literal translation, but the linguistic form of the language, such as tone , syntax, manner of questions posed, and the concept of the consent); and the contexts of use affect meaning. Problems also emerged in bilateral communications – US IRBs may misunderstand local practices, or communicate insufficiently the reasons for their decisions to foreign RECs. In sum, these data highlight several challenges that have received little, if any, attention in past literature on translation of informed consent and study materials, and have crucial implications for improving practice, education, research and policy, suggesting several strategies, including needs for broader open-source multilingual lexicons, and more awareness of the complexities involved.Linguistic and cultural differences can impede comprehension among potential research participants during the informed consent process, but how researchers and IRBs respond to these challenges in practice is unclear. We conducted in-depth interviews with 15 researchers, research ethics committee (REC) chairs and members from 8 different countries with emerging economies, involved in HIV-related research sponsored by HIV Prevention Trials Network (HPTN), regarding the ethical and regulatory challenges they face in this regard. In the interviews, problems with translating study materials often arose as major concerns. Four sets of challenges were identified concerning linguistic and cultural translations of informed consent documents and other study materials, related to the: (1) context, (2) process, (3) content and (4) translation of these documents. Host country contextual issues included low literacy rates, education (e.g., documents may need to be written below 5 The appropriate translation of informed consent and other research-related documents, which accounts for linguistic and cultural variations, is critical to providing informed consent for research. After all, potential study participants cannot reasonably consent to research if they do not understand relevant details of the study and its implications for themselves. Nevertheless, translation of informed consent and other research documents into foreign-language contexts continues to be associated with practical and conceptual challenges.For instance, over 80% of subjects in certain studies did not understand concepts such as placebo, randomization, and the ability to withdraw from research participation without negative consequences to themselves  Despite Institutional Review Boards (IRBs) and Research Ethics Committees (RECs) play vital roles in reviewing studies, including ensuring that consent forms are adequate. Yet they have been found to face considerable challenges, such as those related to resource constraints and lack of sufficient scientific expertise ,6,7,8,9.These issues are particularly important regarding research related to HIV. The pandemic is now most prevalent in the developing world where it can be challenging for subjects to provide informed consent. For example, literacy rates are often low, making informed consent difficult to obtain . FurtherNevertheless, scant attention has focused on how RECs and researchers view and address challenges in translations of informed consent and other study documents.We conducted in-depth interviews with researchers and REC members concerning ethical reviews of research protocols in the HIV Prevention Trials Network (HPTN), a global network funded by the US National Institutes of Health. In these interviews, problems with translating study materials often arose as major concerns. This paper thus presents these data, exploring the specific challenges these researchers and REC members confront concerning translation and how they have sought to address these difficulties.As described elsewhere , we e-maAs seen in Grounded theory also involves both deductive and inductive thinking, building inductively from the data to an understanding of themes and patterns within the data, and deductively, drawing on frameworks from prior research and theories. In conducting thematic content-analyses, we also triangulated methods based on the literature, described earlier. We drafted the questionnaire, drawing on prior research and published literature.After the full set of interviews was completed, three trained research assistants (RAs) and the PI (Klitzman) conducted subsequent analyses in two phases. First, we independently examined a subset of interviews, evaluating factors that affected these respondents’ experiences, identifying categories of recurrent themes and issues, to which we subsequently gave codes. We read each interview, coding blocks of text systematically, to assign “core” codes or categories . A topic name (or code) was inserted beside each excerpt of the interview to categorize the themes being discussed. Then, we worked together to reconcile these independently developed coding schemes into a single scheme. Next, we developed a coding manual, defining each code and examining areas of disagreement until reaching consensus. We discussed new themes that did not fit into the original coding framework, and modified the manual when appropriate.We independently performed content analyses of the data in the second phase of the analysis to identify the principal subcategories, and ranges of variation within each of the major codes. We each identified sub-themes that we then reconciled into a single set of “secondary” codes and an elaborated set of core codes. We assessed subcategories and other situational and social factors. Subcategories included, for instance, specific types of terms in consent forms that participants in HPTN and other studies had difficulty comprehending.We then used codes and sub-codes in analyzing all of the interviews. Two coders analyzed each interview. Where necessary, we used multiple codes. We examined similarities and differences between participants, categories that emerged, ranges of variation within categories, and variables that may be involved. We probed areas of disagreement through closer analysis, and checked regularly for consistency and accuracy by comparing earlier and later coded text. Core codes and secondary codes were systematically developed and well-documented.In this paper, we present issues concerning translation of study materials–particularly informed consent forms. Research was conducted by a primary research team, composed of the PI and three research assistants.To protect confidentiality we have identified respondents not by country, but instead only by ID number and position as a researcher (RES), REC member (REC) and/or a member of a government agency or government REC (GOV). We have indicated when interviewees held more than one position.Overall, as seen in , four brInterviewees described several challenges in obtaining consent–e.g., low literacy and familiarity with science–that were related to social and cultural contexts.Attempts to explain study procedures can also cause confusion within a community being studied. Misunderstandings cause problems not only in obtaining consent, but also because inaccurate or misleading information about a study that can then spread in a community. One researcher described how such miscomprehension led to the “rumor that the research team was going to give the HIV virus to people, and then give them the drug.” (RES #1)Interviewees described how potential research participants may have low familiarity with research or related scientific concepts. “Placebo,” “randomization” and various other terms are “difficult for prospective subjects to understand” (RES #1), and effective communication between investigators and participants is a “big, big issue for us here.” (RES #1)Low literacy levels can pose a series of challenges to a potential subject’s comprehension of a study in developed countries, but frequently become more significant and acute in emerging countries. In certain cultures, written informed consent is difficult “since many participants are illiterate, they can speak the language, but often not read it.” (RES #1) Furthermore, those who can read may not do so at a sufficiently advanced level to grasp major concepts and details that may be included in consent forms. “The US concept of informed consent was too foreign for what happens here. Participants can only listen to so much information at a time,” one researcher stated, “When you have a 20 page consent form, there’s a limit to how much your participant is going to grasp.” (RES #3) Both situations require that information about the protocol be delivered orally, in real time, often with linguistic modifications to accommodate the patient’s level of understanding.” (RES #6) IRBs may thus need to adopt lower levels in certain settings. For studies in the developing country where he worked, one REC chair recommended, for reading level, “going down to fifth grade, at least,” (RES/REC #4), as the standard for study documents, to have subjects sufficiently understand them.US IRBs generally seek that informed consent forms are no more than an eighth grade level–though 92% of informed consent forms the US are too high . As one Obstacles arise due to not only the context in which researchers seek to obtain consent but the process through which investigators try to do so. Specifically, challenges emerge concerning the possible use of oral rather than written consent, the considerable length of forms used, the extensive time that can be required to obtain informed consent, the possible use of witnesses or other third parties, and obtaining individual consent in communities.verbal consent that is ethical and legal…But for the US, you need to have some way of documenting that verbal consent. So I was, in a way, trying to impose US regulations in a country that didn’t have them. (RES #14)If you follow US regulations, you need to have written consent…But there is a concept of Obtaining informed consent using the same process as that in developed countries may appear foreign or inappropriate in other cultures. Given low levels of education and literacy, written informed consent may not be appropriate. At times, written consent seemed an undue imposition of Western culture into a setting where it did not seem to make sense. As one PI explained, cultural differences could lead to accusations of colonialism:This researcher reported that in the country where he worked, oral, rather than written consent prevails in clinical settings–partly because a system exists there such that “medical providers are in this high place, and the rest of the people are very low, it’s a sort of caste-based system.” (RES #14) Based on their experiences receiving clinical care, subjects might thus feel it was inappropriate for the medical doctor to request informed consent, since consent would likely be assumed. Prevalent social hierarchies may thus foster the sense that written consent is foreign, and does not make sense.only oral informed consent. An REC chair recalled that 10 years ago, clinical practice often entailed not having any written consent, because “people don’t know how to read and write.” (RES #14) Another researcher recalled that a few years ago in a non-HPTN study,coercion: they would just test people and not tell them that they were testing them for HIV…I wanted some verification that there had been some conversation with the patient in terms of the consent for the HIV test. I was sort of adamant about that…That’s still going on in many countries, where there’s no written process of consent. (RES #14)There was no consent, it was verbal. There was even Due to the alien nature of written consent documents in certain settings, a few researchers have used However, this researcher felt that there should be flexibility regarding requirements for written consent, “I’m now mellow and more flexible, and as long as somebody documents that the conversation happened, it’s OK with me.” (RES #14) As this interviewee suggested, standards and practices concerning oral consent can vary.The practice of signing forms could also be problematic. Another researcher concurred that in such instances, to require that all participants sign the forms was “not a good way to go” (RES #1).Since written consent documents are often long, some researchers and RECs appeared concerned that the subjects “may be bored before finalizing it.” (RES #15) RECs may thus ask researchers to clarify and shorten the language. As one researcher commented, “Everything could be said with fewer words. It takes too much time.” (REC # 6)Furthermore, to translate a long form accurately into a local language is difficult. To reduce this problem, as one researcher described, a few RECs have instituted a policy of attaching “a summary, in addition to the main [document]” (RES # 5), and they encourage US IRBs to adopt shorter versions of the consent form when possible.more than one day explaining each study, to make sure the person understands fully what we intend. It's a process! (RES # 6)Our patients do not have the same level of understanding…If we really want participants to understand fully what is involved, we tend to spend Subjects vary widely in the amount of time they need to provide informed consent. “Some patients can be consented in one day,” one researcher explained, while “others must call in some relatives or other person and it can take more than one day.” (RES #6) Still, another interviewee thought it could take more than a day. As described by one researcher:witness observe the informed consent processes and ensue that the potential participant sufficiently understands relevant aspects of the study in order to be able to provide informed consent. One interviewee noted that if the person cannot read and write at that level, the ethics committee requires that:Researchers have an independent witness sit in on the informed consent proceedings to make sure the participant really understands. Sometimes a translator sits in on the informed consent proceedings as well…and they’ll sit and listen to the conversation. (REC/RES #10)Some RECs require that an independent Having such parties present in obtaining consent can thus be necessary and/or helpful.At the end of the discussion, I will turn to the educated witness and say, “Do you think this person is understanding what is required of him or her in it, and the data sets?” On three or four occasions, the independent witness said, “No, he doesn’t understand,” and therefore the subject is pulled out as a candidate. (REC/RES #10)Yet researchers may use these translators and/or witnesses in different ways, and to different degrees:not someone who’s attached to the study site.” (REC/RES #10) But that independence may not always be the case. Questions thus surface concerning the specific extent of these individuals’ roles and skills.While witnesses may be asked to make such judgments about comprehension, it is not clear how he or she does so, and how valid their determinations are. Such a witness may erroneously assume that a subject sufficiently understands or not. Clearly, who the witnesses are–their own background, comprehension of English and education and training in research and health care–can affect their decisions. The interviewee above added, “The independent witness is obviously Potential subjects may view consent as more of a collective, rather than an individual activity. Researchers may thus at times need to obtain consent from not only or primarily the individual subject, but also from family or community members. Of note, cultural beliefs may differ to such an extent research design itself may need to change. For instance in one study, cultural views towards HIV testing required that researchers adjust how they approached the issue of confidentiality.…When we talk about HIV testing, you usually test people one-on-one. I know in Africa, whenever the woman was tested first and was positive, she then had to bring back her partner–the couple assumed that whoever first tested positive brought the infection into the family. So the researchers had to modify their testing practices and test people as couples! That’s modifying our culture of confidentiality, because it was not working in that culture. (RES #14)Researchers may thus have to modify their original research design in various ways within the context of the host culture.Questions also emerged regarding the content of informed consent–specifically what exact terms to use to present complex, unfamiliar concepts regarding disease, medicine, research, and human subject protections, given that these terms and/or concepts generally do not exist in the host country language. Challenges arose concerning needs for more than literal word-for-word translation, use of colloquial and vernacular speech and dialects, overall tone and manner, and active vs. passive voice.Fundamental semantic concepts in one language do not always have simple one-to-one counterparts in another. Rather, the meanings of words, phrases and sentences in two languages may be similar in some respects, and vary in others. Yet research documents may not sufficiently reflect these differences. As one interviewee said, “The informed consents were an exact translation from the English language, which was difficult for us to accept at the IRB”, since it did not convey the intended meaning of the consent. (RES/REC #4) In some cases, translation adds meanings that were not present in the original, while in other cases, meanings are lost in translation due to a lack of counterpart in the original language . Consequvirus”, “germ”, “genetics”, “placebo” and “DNA”.Cultures differ in concepts of biology, disease, healing and healers. Interviewees reported having deficiencies in their languages for biological concepts such as “We wouldn’t use something as simple as, “We’re going to take some IV samples,” or, “intravenous samples.” We would say something like, “We’re going to take some labs and some samples,” not “intravenous samples.” (RES/REC #4)Terms about research design and procedures were also difficult to translate in fully and readily understandable ways. A researcher highlighted the need for simplification of such concepts:As a result, some researchers try to use communication strategies such as paraphrase to help potential participants understand complex concepts, circumlocution to facilitate understanding of complex concepts in easier terms, or metaphors to help explain complex concepts by making comparisons or associations between the complex concept and things that on the surface seem unrelated but familiar. Researchers may also add explanatory supports to define concepts. One IRB chair noted that for words that are difficult to translate or understand, like ‘randomization,’ “they always have in brackets a few words to define the concepts better.” (RES/REC #7)Many terms used when obtaining consent relate to human subject protection can pose translation challenges. One researcher said that in his native language, “there is no word for ‘informed’ or ‘translation,’ so it has to be stated in another way.” (RES #1) Concepts may also be interpreted differently. For instance, patients from certain cultures might misinterpret the concept of “study withdrawal” in the informed consent process.If you tell somebody, “you can withdraw from the study any time you choose to,” when you translate it locally, it comes across like: OK, we are not serious here. It conveys, to participants, a lack of seriousness about the researchers’ work. (RES #3)That is, subjects may see this emphasis on the ability to withdraw as suggesting that the researchers do not care much if the participant remains in the study or drops out. Researchers may thus explain concepts in ways that seem straightforward to them, but unintentionally convey such other connotations.Similarly, another researcher worked in a country in which that there was no word for “adherence.” Instead, the word used to convey this notion carried a sociolinguistic meaning of “complying,” which “implies submission.” “There are degrees of power within these words.” (RES #14) Anglicized terms may also enter into a language among educated individuals and be incorporated in consent forms and formed and back translated by local participants.empowerment…That was one of our first obstacles. People have translated it [in a way that] doesn’t sound right or translate to empowerment. It sounds like an Anglicism… I would use that word, but then write in parentheses–the local term for rescuing your control or your power. My concept of empowerment is that it always belongs to you! (RES #14)Another word that’s very difficult to translate is empower also does not exist. Researchers there “use a word akin to ‘confident’”, which carries different connotations. (RES/REC #7) Given the range of these potential misunderstandings, questions arise of what standard should be used to decide when the subjects’ comprehension and consent is sufficient enough. One interviewee stated that he was worried whether subjects truly understood what they were consenting to, including the risks and benefits of participating in the study, and whether they should thus be withdrawn for an inability to give consent–i.e., partly due to translation issues.In another researcher’s country, the word vernacular… A lot gets lost in translation. For example, in translating the phrase “sexual partner,” one reviewer noted that the translated word choice of ‘part-time girlfriend’ was incorrect, and did not capture the idea of “full-time sexual partner”… One REC member, who’s Christian, said, the way the HPTN study described “sexual partner,” “You’re using the wrong word choice. You’re using your part-time girlfriend as opposed to your full-time sexual partner.” Our full consent tends to be at the level of someone who’s got a formal education! (RES # 10)The REC doesn't like translations into Even within a host country, misunderstandings may also exist between RECs and subjects who may be from different educational or cultural backgrounds. For instance, IRBs and RECs may both dislike use of colloquial and vernacular terms , but not recognize the potential importance of these in communicating effectively with local subjects. Miscommunications can thus occur–e.g., regarding the terminology for sexual partners  because the local population may use different, colloquial or vernacular terms than what many REC and IRB members may commonly use:Yet, as the interviewee described, IRB and REC resistance to use of the vernacular can pose obstacles, since those terms may be the ones that many individuals in the local population use and best understand.penis. But in [in another country speaking the same language] is an insect. So, people will say, “Oh! There’s a [word for penis or insect]!” So people start laughing because they don’t understand. So there are a lot of regionalisms! (RES # 14)in [one country], means The study documents may require, too, translations of a word that has very dissimilar meanings in different dialects  of the Such barriers to informed consent can be addressed, but require close attention. As another researcher pointed out, such regionalisms can be avoided, but require recognition and care. “You have CNN in [our language]…and they have no issues with [differences in dialects], so that means we can really get over this problem.” (RES # 14) IRBs may thus try to make sure “that it’s translated into [our version of the language] and not into [another country’s version].” (RES/REC #4)meaning in natural-sounding language, even if this requires more flexibility in the use of different linguistic forms (“the problem is probably not just a word-to-word translation”). (RES/REC #7)Hence, across languages, RECs and/or researchers may want consistent grammatical structure , which is not always possible. As a result, some interviewees felt that translations ought not to be strictly translated word-for-word in many instances, and that grammatical forms need not be always consistent. Rather, translations should prioritize the conveyance of some treatment, to make the tone culturally appropriate. It becomes hard to translate, “this would be expected of you.”… Culturally, we say, “we [i.e. researchers and subjects together] will do this.” It’s almost like you are part of them; you have to make it a collective thing, rather than an individual thing. I understand that the consent has to emphasize every individual’s responsibility separately, but small changes and phrases can make things more palatable. (RES #8)The REC often wants the researchers to change the language, to make it mellow, so that it does not appear that the researchers are ‘talking down’ to the participant. For example, instead of saying “you will have to,” or, “you will give us 5 mL of blood at this visit,” they would say “we would like to request you to give…”…The REC has also always very critical of researchers saying, “we will not be liable for anything or injury” or, “you are not qualified for any compensations.” Researchers are required to say that we will give Challenges also arise in achieving a culturally-appropriate tone and manner. Consent forms are written in formal English, as they are conceptualized as a legal contract. Such a legalistic frame may be a mismatch and off-putting, when researchers are interested in having subjects agree to participate in a study. One researcher noted that RECs often do not like the language used in English consent forms because it comes across as culturally harsh:This interviewee suggested that in ordinary conversations, this kind of request would be framed with a less legalistic, more conciliatory tone; and that a legalistic tone may be inappropriate, and felt to be impersonal. Yet this respondent felt that US IRBs and researchers often fail to appreciate these differences in tone, and in the terms of polite (vs. overly harsh) discourse.Another aspect of formal tone, and written official and legalistic documents is the widespread use of acronyms, which US consent forms also commonly include. Abbreviations of lengthy complex phrases are far less commonly used and less familiar to many participants in their usual discourse and activities. “You tend to use a lot of acronyms in English,” one chair explained, “we don’t in [our language].” (RES/REC #4) Such acronyms can potentially exacerbate participant misunderstanding, confusion, or wariness.It’s also a grammar thing: we do have a passive voice, but we don’t use it in conversation. If we have to translate some consent from English into [our language], and we have to use passive voice, it’s not normal language that we use. (RES/REC #7)As described above, some grammatical structures in English do not have simple counterparts in other languages, and consequently, may not fully translate into other languages, or may require reformulation. One interviewee stated,English uses the passive voice when the agent of the action is not the focus of the sentence, as is common in scientific texts, or unknown; but interviewees felt it was generally culturally inappropriate in consent forms.US IRBs may insufficiently understand local cultures and may communicate poorly with not only subjects but local RECs as well. Problems thus arise concerning both subjects’ misunderstandings of Western concepts, and US IRBs’ comprehension of host cultures.US IRBs and researchers may not understand key concepts and references in the host country culture. As a result, US IRBs may communicate poorly with not only subjects but RECs in the host country. For instance, one host country institution asked for funding for “ers.” The protocol was sent to US IRB, but reviewers did not understand what these were or why they were important. The researcher working on the study surmised that if the sponsoring organization had “on-the-ground experience” and was able to understand the reference, the IRB would have understood that a guardian shelter was “a place the facility put where guardians  can go rest when they’re not by the patient bedside.” (REC/GOV #5) Here, the terms were, technically, translated correctly, but the IRB lacked local knowledge and hence was unfamiliar with the concept.The use of a song in recruitment material was deemed inappropriate by the US IRB. It is unclear why. It might have to do with the lyrics–"because we have to love each other, we are working together”. The song is [in the target language], with the music as a background. But the US IRB said, “This song is not appropriate.” (RES #1)Poor communication can occur between US IRBs and most country RECs concerning not only specific words and concepts, but US IRBs’ reasons for their decisions. Underlying cultural assumptions, norms and expectations can differ, posing challenges. For instance, a cultural incongruence occurred when local researchers wanted to the use a well-known pop song in the local language in a radio advertisement to recruit subjects for a clinical trial. The researcher recalled:Presumably, the US IRB felt that the use of music and a hit song might unduly influence potential participants, and that recruitment materials should be more straightforward, fact-based and unadorned. But the US IRB did not communicate the reasons for its refusal, and the local researcher REC remained baffled and frustrated.Problems can arise during not only the process of obtaining consent from an individual participant, but also the process of translating the documents. Specifically, though researchers commonly have staff perform forward and back translation, these tasks can be highly iterative, requiring large amounts of staff, expertise and time.US IRBs and researchers commonly use back translation to ensure accuracy, but it presents logistical and conceptual challenges. As described above, the process may not, by itself, sufficiently guarantee that researchers are conveying information in ways potential participants can adequately understand. (REC #7)To proceed as optimally as possible, translation and back translation can be a lengthy process . One researcher found no problems in translating informed consent forms from English, but that back translating everything is a, “very labor-intensive process because researchers need to have another person doing back translation–not the one who initially translated.” (RES #15)Unfortunately, some research teams are understaffed in translators, delaying informed consent preparation. Several interviewees suggested that certain countries have a shortage of high quality translators, many of whom are already overextended. In some countries, the translators must “not belong to the project” . (RES #1) Or, researchers may use the same person to do both forward and backward translations; and that individual may also in fact be a research staff member.The process of forward and backwards translations can also require several iterations.it’s a back and forth for me to make them understand that people will not understand these words. (RES #14)If I do a study for a pharmaceutical company, they send this consent translated with weird words that might be used in [one country’s dialect], but are not used in [another country’s dialect]. So three different languages that are spoken there. The researcher described the ensuing challenges related to whether the languages are being translated appropriately:I don’t understand the languages myself, but we have 35 translators to do the translations. We send an English consent to a translator, and always give it back to the study team to look at for appropriateness, to see if it is workable for them and their participants. There are always comebacks in terms of the language not being translated appropriately. There’s nobody there to communicate with the translators to try and find the middle ground that they’re comfortable with in helping the patients understand. (RES #11)A researcher stated that his REC typically requires informed consent in at least To ameliorate these issues, some sites have also begun to develop templates for research documents, but challenges can nevertheless persist as these templates may contain legalese that their IRB or REC requires.We already have a template here because we have been doing this for a long time. We also have some paragraphs with legal things that we cannot change, like “the university will not compensate you for damages, but will provide you care, free of charge.” We have to include these paragraphs. They are non-negotiable… But I already have a template for the consent in both languages like: the purpose of the study, how many people, the essential elements of a study, so that we can match. (RES # 14)To try to address these problems, some researchers have also asked Community Advisory Boards (CABs) that they have established to “help modify some of the words and language” in the informed consent process. (RES #14)Though challenges concerning the translation of informed consent and other study documents in research have been discussed for over 30 years ,18, sevePast literature about translation of informed consent and research documents has tended to focus almost exclusively on literal, word-for-word translations. Researchers have, for instance, begun to develop lexicons to aid in translation of research documents . Yet ourSpecifically, these data suggest that misunderstandings can occur related, firstly, to terms ; and secondly, to tone and voice. This typology can aid future efforts to improve translation of study materials, by focusing endeavors, and facilitating further consideration of potential relevant terms in each category by researchers and IRBs. Cultures may traditionally have different concepts related not only to medicine and healing, but to implicit expectations concerning the roles and responsibilities of healers and patients ,15.Linguists and others have explored how words that translate each other are not necessarily isomorphic in meaning and function , “There necesito tomar sangre (“I need to take blood”) since tomar is typically used to mean “drink” with liquids. The extraction of blood in Spanish is more accurately expressed by extraer (“extract”) or more familiarly by sacar (“take out”). Also, a Spanish-speaking physician would also probably substitute una muestra de sangre (“a blood sample”) for the generic term “blood” [The meaning of a particular word, for instance, may depend to a large degree on the context of other words. Language itself can in fact shape concepts that cannot be easily or well-translated from one language to another due to nuances, causing awkwardness. For example, one can say in English “take blood” or “draw blood” to express “the extraction of a sample of blood.” In Spanish, however, it would be quite odd for someone to say  “blood” . FailureConsent forms may require translations of words that are not typically used in conversation, and can be awkward and/or difficult for potential subjects to understand. Also, typical of scientific language, consent forms contain numerous examples of noun compounds, consisting of two or more nouns in sequence , that have no exact equivalent in many other languages, and thus often need to be rephrased .The fact that consent forms are often perceived as legal contracts creates other problems, since that requires the use of certain language features to convey clarity and directness concerning the study purpose and risks, the conditions for participation, the subject’s rights, and participation. These linguistic characteristics are phonological , lexical, syntactic , and pragmatic. Yet these characteristics may make many participants feel mistrustful or wary.Use of the passive voice also poses problems. Consent forms may contain sentences such as, “This is a drug that has been approved for treating drug users in the US” that use the passive voice because the person doing the approving was unknown and unimportant in this situation. Consent forms often contain sentences such as, “The treatment you are given will be decided by chance”–in which the focus is more on the treatment and the decision process than on the people who are giving the treatment or making the decisions. Hence, the passive in English is used.English does not share these uses of the passive voice with many languages. Other languages may use to express similar meanings different structures, such as reflexives or impersonal expressions , creating translation challenges. These issues can occur within a single linguistic community as well, but get compounded when translating documents across different cultures, due to differences in norms, education and other factors.formal aspects of the consent process. In many instances, oral consent may be appropriate. However, oral translation, and discussion about consent, including responses to participants’ questions that may arise during the consent process, can consist of casual conversations that employ metaphors, idioms and vernacular. Yet extensions of meanings can result from the unintentional use of idioms or metaphors , and have connotations that are only derivable from the context in which the word is used . These implied connotations can relate to contextual meanings , sociolinguistic meanings , sociocultural meanings , and psychological meanings  [These data highlight challenges concerning not only the content, but the methods and the  stance) ,26. A tr stance) . The curThese data also suggest challenges concerning what oral consent should include–whether to standardize or assess the informal conversations that may ensue between participants and researchers, and if so, how. These findings suggest needs for empirical studies of what researchers say to subjects when oral communication is used, either alone to obtain consent or in conjunction with a written document.This study highlights, too, the potential benefits and importance of having independent witnesses involved in the consent process. Witnesses have received a relatively small amount of attention in the past scholarly literature. An independent witness can potentially improve informed consent , yet sevWestern researchers and IRBs may also not recognize in advance how frequently extended periods, even whole days, may be needed to obtain consent–when, with what proportion of subjects, and how, and exactly what information to present.These data suggest, too, problems of bidirectional translation. US IRBs may not only misunderstand key aspects of the local language and culture, but fail to appreciate this misunderstanding and its extent, and how much potential participants may misunderstand study documents. US IRBs now seek to have consent forms be at no more than an eighth grade reading level, but fail to do so 92% of the time . Yet theWestern researchers and IRBs need to be acutely aware, too, of contrasting views regarding even the perceived value of consent, and should consider carefully how to present and frame its importance–perhaps doing so orally in ways that these IRBs and investigators script and review.Our data have several implications for future efforts to enhance participant comprehension. Mack et al. used 12 focus groups with women in Tanzania to grasp their understandings and preferences concerning research terms in HIV studies . The preOur findings suggest how such efforts should be broadened to include additional words, as well as discussions of common linguistic structures in consent forms  that may not have equivalents in other languages.The lexicon by Ramirez et al. presents words alphabetically, but might also be divided by categories of words or concepts–e.g., following the typology presented above involving categories of disease, disease mechanism, research design and subject population  to help to ensure thoroughness, and facilitate the further development of such efforts. Lexicons could also be expanded to include additional languages of the 6,000 spoken today  and dialAnother possible model would be to develop an online dictionary similar to WordReference –an open Reference tools could also be developed to contain template language that is both amply back translated and culturally appropriate for a wide variety of host countries.Our data also suggest that such an expanded lexicon could help with some, but clearly not all of the problems identified in these data. A word-for-word lexicon may not be readily capture contexts that can be crucial in interpreting the meanings of particular words. The lack of direct literal translations can also substantially increase the length of informed consent forms  do not have grammatical tense. Thus, guides should be developed to accompany lexicons to include relevant in-depth information about more than word-for-word translations–concerning appropriate tone.These data pose larger questions, too, concerning the tone that consent forms should use. Many IRBs seek to employ language and tone that they feel is appropriate and professional, but that subjects may feel is too legalistic, harsh, or impolite. Interviewees suggest that US IRBs often seek to avoid use of colloquial or vernacular language. But at times, colloquial language may aid subject comprehension; and the boundary between vernacular discourse may be blurry, rather than rigid. In many instances, both types of discourse may be beneficial and used by subjects. Some local researchers thus put various terms in brackets. Yet, US IRBs may sometimes fail to appreciate these complexities, and may not want to include more than one possible translation of a word. Further research should explore how often and when researchers add such terms, how doing so may aid subject comprehension, and how IRBs respond.These data also underscore how the process of translation can be very time and labor intensive. Researchers should thus plan adequate time, and be fully aware that back translations may not always capture problems. On-line hyperlinked informational platforms in which subjects should click for more information about any particular topics might also be helpful and worth developing. These efforts may also require additional resources. USAID  funded texplanations for their decisions to foreign RECs . US IRB make decisions for reasons that may be culturally specific in ways that foreign IRB may not understand. US IRBs could thus seek to improve communication with not only foreign participants, but foreign RECs through appropriate cultural translation is needed of IRB memos, too.These data also elucidate challenges in bidirectional cultural communication and translation from local languages to Western cultures. At times, US IRBs may need to communicate more effectively These data highlight as well needs for broader open discussions concerning how informed consent forms can both satisfy the needs of a legal system in Western cultures and account for cross-cultural sensibilities. These interviews underscore critical questions concerning whether at times Western notions of informed consent may represent ideals that in current practice may not always be fully attainable. Clearly, researchers should still pursue these ideals as much as possible, but may not always fully succeed .These data further highlight the need for heightened connections between research ethics and linguistics, which appear to have been limited heretofore. Much of the literature concerning theories of translation has focused on works of literature, and the difficulties of achieving “faithful” translation . Prior rUS IRBs may also need to recognize more fully their own cultural embeddednes–how much they themselves may make certain assumptions that foreign study participants and RECs may not share.This study has several potential limitations. These data are based on a relatively small number of in-depth interviews with investigators who conduct HIV studies in developing countries and REC personnel outside the US; however, future studies can examine these issues among larger samples of investigators, REC personnel and others in these and further countries, examining research on other disorders, too. However, the experiences here suggest that obstacles exist–e.g., in contacting and entering into studies REC members who have reviewed specific protocols. Of the 15 respondents, 5 were REC members or chairs. Though many of these interviewees are researchers, their first-hand experiences and perspectives are vital to understand. The number of themes raised in these data may seem relatively high, but these respondents each ended up discussing a wide range of topics and protocols. These data also explore these interviewees’ experiences and perspectives at one point in time, but future studies should investigate attitudes prospectively over time and do so among larger numbers of participants to assess whether their views alter over time.In sum, the data from the current study suggest a variety of challenges that US sponsors and researchers doing research abroad face. As clinical trials in emerging economies, related to prevention and treatment of HIV and other disorders become more common, fuller, deeper and more nuanced understandings of the bilateral, linguistic and cultural challenges involved are crucial. These understandings can help in the development of improved researcher and REC practices in translating and obtaining informed consent, and enhancing other aspects of studies.S1 Data(DOCX)Click here for additional data file.


Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers' choices and designers' understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons. The emergence of Web 2.0 technologies and the growing number of online reviews websites, such as Amazon, Epinions, and Cnet, emphasize user participation. People are encouraged to express their opinions/sentiments on purchased products. Sentiment analysis  is a natural language processing task that aims to track the public's mood regarding a particular product or service. This type of text analysis belongs to the field of natural language processing, computational linguistics, and text mining . It is cAll opinion mining approaches are performed on reviews, which can be (1) regular or (2) comparative. The review types are differentiated based on language constructs that express diverse kinds of information . RegularSuggestions are indirect speech acts. Kumar  explainsS = 〈i1, i2,…, in〉, where every item corresponds to a feature that belongs to a certain token. The fundamental notion is a mixed sequence approach to achieve high recall and to build a supervised machine learning model to automatically classify each sentence into two classes: (1) suggestive and (2) nonsuggestive, in order to enhance precision. To sum up, the contributions made through this study are as follows: (1) propose a study of the problem with classifying suggestive sentences in text ; (2) categorize suggestive sentences into different types based on linguistic research; (3) perform sentiment analysis on classified suggestive reviews.Generally, suggestive sentences employ quite different language constructs from typical opinion sentences. Hereby, the aim is to study the issue of identifying suggestive sentences in text documents, for example, consumer reviews of products like movies and cell phones. The issue is challenging because although it is obvious that the above example sentences all contain some suggestive indicators, their semantic is not as suggestive as, “I could go to buy this phone.” Similarly, many sentences do not contain such indicators and are still suggestive sentences, like for instance “He might help you.” We first classify suggestive sentences into different categories based on existing linguistic research and then expand them into additional categories that are essential in practice. Subsequently, we propose a novel approach based on mixed sequences and supervised learning to identify suggestive sentences. A sequence of items is denoted as An effective approach to solve the drawback with feature construction using mixed sequences and the machine learning technology is thus proposed. This paper is organized as follows: in Related works from both computer science and linguistics are explored. Researchers in linguistics focus primarily on defining the syntax and semantics of suggestive constructs. Human communication is a broad phenomenon with multifarious facets and dimensions, including all signs whether textual, nontextual, verbal, and nonverbal. The majority of human communication occurs by means of utterances. According to linguists, suggestives are speech acts. The speech act theory was put forth by John Austin in his lectures published on how to do things with words . CrystalThus, language not only conveys information, but there are acts carried out or performed through words. Cook  elucidatAmong the examples provided by Crystal to illustrate indirect speech acts are requests and suggestives. Consider the following example by Crystal: “It's getting cold in here, Shall we keep out the draught?” . This spIn text and data mining, no direct work on suggestive sentences has been identified so far. Sentiment classification and opinion extraction are closely related but differ from our work. To classify stock postings, a manually crafted lexicon in conjunction with several scoring methods was employed by Das and Chen . SeveralIt is aimed via the work reported in this paper to fill a notable gap by offering designers and customers a possibility to process vast amounts of user input in the form of suggestive reviews. Initially, a linguistic view of suggestives is provided and some limitations are identified. They are then enhanced by classifying suggestive examples into suggestives with implicit and explicit locutions, as well as performing sentiment analysis on the suggestives.As mentioned earlier in this discussion, suggestives are speech acts. More specifically, they are indirect speech acts. According to , speech It is deduced that the opening keywords and phrases in suggestives are followed by infinitive verbs. Thus, constructions are should + infinitive, let us + infinitive, shall I + infinitive, do you want to + infinitive, why do not we/you + infinitive, we could/you could + infinitive, and ought to + infinitive. Clearly, most suggestive constructions follow a key suggestive word + infinitive verb structure. Nonetheless, there are exceptions, for example, I/we suggest/propose (suggest/propose that + subject + should + infinitive), if I were you, and what about/how about? (Key phrase (what about/how about) + gerund + noun). The above linguistic classification of suggestive sentences has two limitations: (1) nonsuggestive with suggestive words ; (2) limited coverage or suggestives with nonsuggestive words .To address the first limitation, computational methods will be used  to distinguish suggestive from nonsuggestive examples. As for the second drawback, several feature sets and the way they are written in combinations of other words potentially indicating suggestions will be employed. The performance characteristics of additional knowledge will be portrayed besides the bag-of-words representation and experimenting with a different, more flexible representation called Mixed Sequential Rules that is evaluated within a machine learning framework. We also test the effect of feature selection. For sentiment lexicons, the effects of target masking, negation handling, and lexicon features are investigated. r, predicts its class, c, where c is a regular opinion, comparative or suggestive; task (2) given a suggestive review, r, predicts whether it is a suggestion with implicit or explicit locution; task (3) given a review, r, predicts whether its sentiment is a negative, neutral, or positive. As discussed earlier, a suggestive sentence is indicated both via specific keywords or more complex phrases and syntax patterns. Therefore, we have framed this problem as a supervised learning framework in an attempt to train a classification model capable of predicting whether a particular sentence contains a suggestion based on existing examples, which have already been classified as suggestions or not. In order to achieve superior classification performance and discover useful feature sets, we experiment with several combinations of feature sets and classification models. The feature sets are designed to accommodate existing state of the art in text classification, along with novel feature sets that utilize linguistic intuition behind suggestive statements. First, the various feature sets employed in this evaluation are described. However, suggestions may also exist without an explicit suggestion word. Similarly, nonsuggestive sentences can contain a suggestion word. In order to accommodate these nontypical cases, a machine learning approach that works on a wider feature space, looking beyond suggestive keywords, is applied. Such methods do not merely identify features that are indicative of a suggestive sentence, but also counter-indicative features, meaning they can indicate that a certain sentence is definitely not a suggestion.In the present work, suggestives are studied at the sentence level. Thus, the problem is stated in view of sentences. Task (1) given a review,Since every statement in its raw form is represented as a sequence of words, it cannot be fed directly to the learning algorithms themselves, as most expect input in the form of real-valued feature vectors with a fixed size rather than raw text documents of variable length.(i) tokenizing strings and giving an integer identifier for each possible token by using whitespaces and punctuation as token separators;(ii) transforming the individual tokens for the purpose of data cleaning. In this step, all words are lowercased and stemming is applied via the Snowball stemmer ,” and every mention of another product is replaced with “[Other];”(iv) negation processing, transforming every word w in a window of length 2 after a negation word into a “not-w” token. With this technique, the notation “Neg” is used. Literature demonstrates that this particular preprocessing approach can improve sentiment analysis performance [formance ;(v) filtering the tokens in order to remove words with low information value. All tokens which occur less than three times in the training set are filtered;(vi) counting how many times word tokens occur in each document.To address this, the following preprocessing approaches are used:(i) Each individual token occurrence frequency  is treated as a feature.(ii) The vector of all the token frequencies for a given document is considered a Multivariate sample.Features and samples are defined as follows.X with a row for each document and column for each token  taking place in the corpus. The value of cell xij represents the number of times the word j appears in the example. In our evaluation, this feature set is denoted as “Bow” (bag-of-words). Since this approach is widely used in text classification, this feature set is deemed to be a baseline. Within preprocessing, part-of-speech tagging is performed as well, which is employed in other feature sets. The Maxent tagger, trained on the Penn Treebank features, is utilized to produce the part-of-speech tags [Description of documents depends upon word used in it with little or no care of where the word is used in the document. This follows that a corpus of documents can be depicted through a matrix form, whereby matrix ech tags .number of fully capitalized words in review “This phone is AWESOME” generates a value of 1.0;number of question words  “Why and how is this broken?” generates a value of 2.0;number of negation words  which are words that negate meaning and can often affect the interpretation of the whole sentence, as revealed in the task of sentiment detection ;number of contrast phrases  which are words that signal the presence of a contrasting statement and often imply that the user is evaluating a certain object;number of question marks. In the evaluation, this feature set is denoted as “Surf.”This feature set covers peculiarities that may appear in informal texts, such as social media or public review boards. These features focus mainly on the style of expression and less on the content itself. This feature set is included to determine whether it carries any interesting information for suggestion classification:Given the tokenized content annotated with part-of-speech tags, features are generated based on the presence of patterns given the theory described in Comparator keywords were successfully combined with class sequential rules for the task of detective comparative sentences . Since cTo take into account the background theory on how suggestions are presented, a lexicon of performative verbs is additionally included . We hyposum of all positive sentiments of all words;sum of all negative sentiments of all words;total objective sentiments of all words (where objective = 1.0 − (positive + negative)) score;ratio of total positive to negative scores for all words.Only for the sentiment analysis task, external knowledge is integrated in the current model by means of a sentiment lexicon. In particular, we use SenticNet, a knowledge base that maps a sentiment value from −1.0 to 1.0 to a set of words or multiword expressions . The folIn addition to total sums, the same features for nouns, verbs, adjectives, and adverbs are also generated. Besides providing total sums, these features are also produced for nouns, verbs, adjectives, and adverbs separately. This feature is set as “Lex.”S = 〈i1, i2,…, in〉, where every item corresponds to a representation of a certain token. Recent methods for comparison classification have successfully used sequential patterns of part-of-speech tags surrounding certain keywords [A sequence of items is represented as keywords . The resn that contain at least one word of interest. Then the feature is generated by combining the literal representation of the word of interest, and the surrounding tokens are represented by their respective part-of-speech tags. The following rules define various classes of words of interest for generating sequential rule features.Every verb is a word of interest. For instance 〈“buy,” “this,” “camera”〉 becomes “buy_DT_NP.”Every adjective is a word of interest. For instance 〈“buy,” “this,” “good” “camera”〉 becomes “VB_DT_good_NP.”Every adverb is a word of interest.Every suggestive keyword is a word of interest.Every comparative keyword is a word of interest.A mixed sequential rule feature generator is defined by two parts: a word of interest and its surroundings. Initially all token sequences are taken with lengths up to Through this procedure, all subsequences of lengths up to 3 are generated. Each occurrence of a sequence rule in a given example generates a single feature corresponding to that sequence rule.X, with n rows and d dimensions, where “n” is the number of examples and “d” is the number of known features in that particular feature combination.In order to join the contribution of multiple feature sets, features are combined by performing a union of all features on every individual example. For instance, in a “Surf + Sug” feature construction setting for the example “This is an AWESOME phone, you should buy it!” the following would be generated: {“should”: 1.0 “fully_capitalized_words”: 1.0, “exclamation”: 1.0}. In the end, every known feature is enumerated and assigned a dimension in the vector space model, so that every column corresponds to a feature and every row corresponds to a review. The dataset is therefore represented as a matrix f, and the class, c, given the training data which could be used to estimate extremeness based on Chi-square distribution with one degree of freedom. This feature selection approach selects only the features which are least likely to be class independent.So as to reduce dimensionality and noisy features, applying the Chi-squared feature selection criterion  and mainX and an external variable that we are trying to predict, usually called target or label and is denoted as y, a one-dimensional array of class values for the examples. Support vector machines using the linear kernel are employed for experimentation [L1 regularization also partakes in testing [C parameter) values. For instance, LR_C5 means that LR with C = 5.0 is applied.Supervised learning consists of learning the link between two datasets: data entation , as it h testing , and thiThe entire pipeline is visualized in using suggestive key phrases improves performance;using comparison key phrases improves performance;using performative verb detection improves performance;using the mixed sequential rules representation improves performance.The baseline representation is a straightforward bag-of-words representation; one that we endeavor to improve. The hypotheses to be validated are:2), logistic regression (LR), standard vector machine chi-square (SVMChi2), and standard vector machine (SVM) are assessed. Each experiment is annotated by the feature sets and learning algorithm employed. The feature sets are marked with Bow, Cmp, Sug, Surf, and Mix. Where in case an experiment is marked with Bow + Cmp + Sug − SVM it means that its feature space is an amalgamation of bag-of-words features, comparison phrase indicator features and suggestive phrase indicator features, with SVM as the learning algorithm. These features are then transformed into a matrix representation and serve to train the learning model. The approach is evaluated on three classification setups:suggestive sentence detection: given an example, classify it as either suggestive or nonsuggestive;comparative sentence detection: given an example, classify it as either comparative or noncomparative;expressed/implied locutions detection: given an example suggestive sentence, classify it with either having expressed locution or implied locution.With the intention of discovering the key features, several different feature set combinations are evaluated. Besides, several supervised learning algorithms and their hyper parameters such as the logistic regression chi-square  were calendar, yet but, to text, fun, inches, not great, way of, let's, recently, and impressive. While these features make a well-performing classifier, they simultaneously remain very typical for the general topic of phone reviews; few of the features would successfully generalize to other domains.Once a linear model has been trained with training data, its coefficients of individual features for every binary classifier are noted. For a given binary classifier, it is possible to observe whether an individual feature is indicative of the class or counterindicative—in which case its presence puts it in another class. For the task of classifying suggestive sentences in a bag-of-words model, the features with the lowest weights  wereought to” has a slight negative weight. This confirms that the theory holds true in this dataset but that some of the rules may benefit from restricting their context. For instance, “ought to” can also be used in a 3rd person scenario, where the reviewer is describing a hypothetical aspect of a device and is not actually giving a suggestion.As for the weights of a model trained only on suggestive clues, almost all of the clues have positive weights, whereas only “ VB_good_., IN_more_than, charg_TO, CC_IN_was, should_be_ad, and realli_JJ are observed. These indicate qualitative device descriptors. Conversely, the suggestions are specified by features such as PRP_lock_RB, use_CC, will_need_TO,_go_RP, and catch_more_NN, patterns that, interestingly, indicate device usage. Following that, people who suggest a device also tend to more vividly describe themselves as using it. For the problem of characterizing suggestive sentences by classifying them into implicit or explicit locution, the model's behavior when using suggestive and comparative features is noted. Comparators indicate implicit locution, and suggestive clues tend to indicative explicit locution more. This confirms the theory that when a reviewer explicitly recommends a product, they are likely to use more explicit terms, such as modal verbs. Upon viewing the same task on a different feature set, the best-performing mixed sequential rules, the pattern VBZ_not, RB_worth_DT, and TO_use_CD signifies implicit locution, while forget_TO_put and camera_is_RB are more indicative of explicit locution.With regard to the weights of a suggestion detection model consisting of mixed sequence rules, when looking at the set of features that indicate nonsuggestions, patterns such asA study of suggestive sentence classification and polarity detection was proposed in this paper. Different types of suggestive sentences were first analyzed from both linguistic and practical usage points of view. Aiming at identifying suggestive sentences, subsequent analysis was carried out through rule mining and machine learning approaches. The new approach was found effective by empirical evaluation based on mobile datasets.Based on a comprehensive literature review and discussion, it can be argued that we spark a new debate on analyzing online product reviews. In order to construct models with outstanding performance in both suggestive and comparative tasks, the use of several feature sets was evaluated. Such feature sets may serve in detecting and characterizing suggestive sentences, and the combinations of various feature selection and supervised machine learning approaches were used. It was shown that for the task of detecting suggestive sentences, the basic bag-of-words model can be enhanced by augmenting it with additional features, especially with suggestive and comparative clues. The same approach also worked best in detecting comparative sentences. However, the bag-of-words representation may potentially lead to very topic-specific models, which may not translate to other domains, as indicated in the feature analysis. A combination of just suggestive and comparative keywords offers a high-precision alternative which can potentially also generalize better.We would like to emphasize that, besides classifying suggestive reviews further in to explicit and implicit, we came across yet another novel type of reviews within this family. Although the finding of this type of review was not the main focus of this work, we have tentatively identified and reported it in this paper. Due to the lack of sufficient sample size  we are cautious at this stage to classify these reviews separately within suggestive family. However, we do report this class for raising future research avenues based on larger sample size and deeper focused analysis on this type of reviews.With respect to comparative sentence classification, no improvement was discerned when using a lexicon of performative verbs or a mixed sequential rule representation of the content. Nevertheless, it was demonstrated that in view of separating explicit and implicit locutions combining only the set of suggestive patterns with a lexicon of performative verbs offers optimal performance. Such performance level can be matched by training with mixed sequential rules. This indicates that flexible data representation is viable in instances where domain knowledge is unavailable. Since the dataset consists of a rather small number of examples, the hypothesis that feature selection is beneficial for both classification problems is confirmed, particularly when employing higher-dimensional representations based on the bag-of-words model. With regard to sentiment analysis, the mixed sequence features proved to be a promising base feature set and sometimes outperform the bag-of-words feature set. Overall, the best strategy for this type of dataset was to employ negation handling and target masking on the input text and augment the bag-of-words feature set with additional lexicon features. From the perspective of learning algorithms, both logistic regression and support vector machines perform comparably well in the three issues discussed before.Big social data analysis  plays a


AbstractThe marketing approachPrinciples of Marketing, 2nd edition, Teora, Bucharest, 2003, p. 4). However, this does not mean that the only purpose of marketing is client satisfaction, regardless of the manufacturer’s expenses. In the same place, Kotler and Armstrong offered the simplest definition of marketing: “marketing is the delivery of client satisfaction at a profit.” (id).In reply to the question “What is marketing?”, Kotler and Armstrong wrote: “More than any other company function, marketing deals with clients. Creating value and satisfaction for the client represents in itself the essence of modern marketing thinking and practice” .Therefore, modern marketing places the Principles of Marketing, Kotler and Armstrong define marketing as it follows: “a social and entrepreneurial process, through which individuals and groups obtain the thing they need and they desire, by creating and exchanging products and value with other groups and people” (id). It is a social process – an extremely complex process, and sale is, like advertising, only one of its many functions.In Introduction to Communication Sciences, John Fiske showed that in the semiotic approach the message is a construction of signs, which, by interacting with the receptor, generates meaning. The focus is not so much on communication as process, but rather on communication as generator of meaning. The sender (message transmitter) loses his importance. The focus is directed towards to the “text” and the way it is “read”. “The reading” is the process of discovering the meaning that emerges when the “reader” interacts or negotiates with the “text”. The negotiation takes place when the “reader” filters the message through the strainer of the cultural pattern, in terms of signs and codes that make up the message. The more we share the same codes and the same sign system, the closer the two significances attributed to the message .In a classic paper, paradigm and referential respectively).When we think in the terms of semiotic approach, which highlights constantly, biunique interactions between the message “producer” and the reference system, between him and the “reader”, we deal with the cultural determinism of communication, using the concepts of Kuhn and Gonseth .The concept of “cultural paradigm” has been used increasingly more over the last four decades, both in social philosophy, as well as in anthropology, psychology and sociology . It entered these fields by way of “concept translation”, borrowed from the philosophy of science, where it was imposed by the American philosopher Thomas S. Kuhn. He was the one to realize that theories on the nature of science and the purpose of research in natural sciences do not concord with the scientific practice, as it ensues from the history of science. In practice, he says, the behavior of scientists deviates from the canons that define use tacit communication, which could reach for the “self-image” of groups and individuals from various social groups, in order to trigger the change of some of the current cultural paradigm’s presuppositions, in particular of those who generate perceptions, representations and value-attitude couples, which, in their turn, generate contra-productive behaviors (which oppose the purposes of modernization). In essence, it is the art of talking about something while leaving the impression that you talk about something else. The only realistic solution is to modernization of the Romanians, a much more complex and difficult task than education.To those who will cry resentfully “This is manipulation!” we reply: 1) manipulation “abhors vacuum”, because if we do not manipulate, others will; 2) assuming nevertheless that no one is manipulating them, people would manipulate themselves- which they do, “day after day, hour after hour, and in mass”, by virtue of desiderative thinking, of inauthentic thinking (Erich Fromm) and of the “voluptuousness of self-deception” (Jean-Francois Revel); 3) manipulation is not evil in itself; it can be good or bad, depending on the purpose; 4) nothing great can be accomplished without manipulation – from bringing up a child to educating a people -, and we do not speak of the Conclusion: he who finds himself from manipulation is manipulating himself, but against him; he remains a «part of the problem and will never be a “part of the solution”».value-attitude couples that are at work in today’s Romanian society, knowledge that will be ensured by mixing theoretical approaches with national-scale sociologic research, carried out by a specialized institute.A pre-condition for success is the positive knowledge of Keywords: marketing and semiotic approach, communication, target audiences


PLOS Genetics readers, irrespective of gender or generation, will also find stories and ideas in these books that connect to their own professional and personal experiences (One of the often-bemoaned characteristics of modern life is the absence of leisure time due to chronic “busyness” and the constant connection afforded (or required) by our ever-present digital devices. As a result, time for nonessential reading is often in short supply, and we fiercely guard any minutes that we can steal from the other commitments of life. So when we do find time, what do we read? As the literary critic Harold Bloom said, “We read to find ourselves, more fully and strangely than we otherwise could hope to find.” Therefore, perhaps not surprisingly, when I get time, I read about women in science, past and present. Unfortunately, there are remarkably few books about the experiences of non-Nobel-Prize-winning female scientists, so I’ve had to look pretty hard to expand my reading list. In this Deep Reads article, I recommend four books about women and science that I read in 2015 (“When did you have time to read four books?” some will no doubt wonder). My hope is that eriences .Profiles of great scientists chronicle not only their discoveries and career paths but also other dimensions of their lives and personalities. Too many profiles of great women scientists, however, have focused on their exceptionalism, too frequently treating the “subject’s sex as her most defining detail. She’s not just a great scientist, she’s a woman! And she’s also a wife and a mother, those roles get emphasized too!” . In 2013Science journalist Christine Aschwanden developed the “Finkbeiner Test” in response to the Brill obituary and this kind of journalistic bias. To pass the test, a profile of a woman scientist must not mention “the fact that she’s a woman, her husband’s job, her child care arrangements, how she nurtures her underlings, how she was taken aback by the competitiveness in her field, how she’s such a role model for other women, or how she’s ‘the first woman to…’” . AlternaWhile it is true that Brill’s obituary is an example of an absurdly skewed focus on women scientists as women first and scientists second, I think that it would be unfortunate and counterproductive to remove all references to other aspects of a scientist’s life, female or male, from an obituary or profile. There is an interesting and productive way to describe how a person, especially a woman, balanced myriad and competing expectations and obligations, especially social and institutional barriers and attitudes. This is the nuance of good biography and narrative, interwoven with social history, and to surgically remove certain aspects would be to tell an incomplete story. By examining the lives and struggles of successful women scientists in the past, we can learn about what needs to be done and what works to make more women successful in science in the future.Headstrong: 52 Women Who Changed Science and the World, Rachel Swaby takes an intentionally different approach. She profiles 52 women scientists across a range of disciplines, including nine women in genetics and development. Swaby only includes women who have already died, acknowledging that this limits the diversity of her subjects. Nonetheless, their stories share common themes across disciplines and time: adventurousness and passion for science; persistence and perseverance in the face of obstacles; the influence of a supportive parent, mentor, collaborator, or spouse; battles over credit or even the grudging realization that credit for discoveries would likely never be forthcoming; reinvention and recovery from setbacks; creativity and innovation in dealing with a lack of title, resources, and lab space; and research on potentially unpopular topics for which the competition was less intense.In PLOS Genetics reader will likely have heard of some of the women and not of others, but even the familiar scientists are presented with unusual stories or perspectives. Headstrong is an easy and fun read about a fascinating and tenacious group of women. It is an encouraging reminder of the largely untold legacy of women in science and their supporters, and it provides a connection between some of their challenges and those of the present day.The short, three- to five-page profiles are introductory and include only a few narrative highlights, thereby missing some of the subtlety and nuance that would exist in a longer biography or even a dedicated article, which each of these women surely deserves . The Mary Ingraham Bunting: Her Two Lives by Elaine Yaffe. The book recounts the multiple stages of the life of Dr. “Polly” Bunting: from early bacteriologist, to stay-at-home mother and occasional researcher and teacher, to sudden widow and single mother, to Dean of Douglass College (the women’s college of Rutgers University), to President of Radcliffe College, to founder of the Bunting Institute, and, finally, to the first woman on the Atomic Energy Commission.A more in-depth biography of one 20th century woman scientist and leader is Bunting’s early life and young adulthood were characterized by the pursuit of a career in research science at a time when there were still significant institutional barriers to training, employment, and funding for women. Yaffe writes, “It was always her temperament to look on the positive side of things and to ignore unpleasantness,” and this attitude served her well throughout her life. The book is filled with examples demonstrating her improvisation and common sense, her juggling of time slots and commitments at work and at home, her efforts to break down complex problems into their component parts, and her patient efforts at brokering compromise and defying expectations.Serratia as an exceptional example of bacterial genetics [Of particular interest is the description of Bunting’s invitation in 1946 to the Cold Spring Harbor Laboratory Symposium, organized by Salvador Luria, to present her pre-war, pre-motherhood research on color mutations of genetics . Even thBunting wrote the paper for the symposium with the support of a babysitter every Wednesday night, and her husband Henry took off several weeks in June so that she could focus on the work. She faced other challenges: during this time period, she also contracted German measles, and the family had to sleep in a 10-by-14-foot goat house on their property while their home was under construction. At the conference, Bunting was the only woman among the 26 invited presenters. While there, she networked with other participants to coin the phrase “microbial genetics” to describe the new and emerging field. The experience, combined with a stint of teaching bacteriology at Wellesley College, “reminded her how intensely she loved science and how hard it was to combine research with caring for small children.”It would be years until Bunting returned to research part-time, and almost nine years until her husband died unexpectedly, forcing her to seek full-time work and shift to focus on the college education of women. But these early experiences made her acutely aware, even decades later, of the unique challenges faced by women who hoped to combine raising a family with the demands of a career in science. She dedicated much of her work later in life to developing innovative approaches to helping women transition back into the workplace after child-bearing and -rearing and to the gradual dissipation of the “climate of unexpectation” for women.The Only Woman in the Room: Why Science is Still a Boys’ Club by Eileen Pollack, rose out of the author’s experience as one of the first two women to major in physics at Yale in the 1970s [The third book, he 1970s . The firThe most compelling part of the book, however, is the second half, based on six years of Pollack investigating what has and hasn’t changed in science for women in the intervening years. She interviewed former teachers and classmates, current professors , and women science graduate students and post-docs. I was interested in her discussion of the subtle skills required for success, now that women in science are no longer anomalies. Specifically, Pollack provides an excellent examination of current tensions around expectations and both discouragement and encouragement from mentors and society.Dr. Meg Urry, the first female Chair of the Physics Department at Yale, shared with Pollack her opinions about the role of confidence, or a confidence deficit, in scientific success for women. She observed that women are frequently socially conditioned to be less confident:Over and over, Meg has seen women with above-average abilities drop out of science, while “more men of mediocre talent are determined to prevail…and they do.” It would be one thing if the weed-out system worked, she says. Then, even if the system weren’t kind, you could defend it. “But it just isn’t true. Lots of mediocre people have shitloads of confidence, and there are many brilliant people who have no confidence.” Even if you don’t start out being confident, “you can grow the boldness you need to be a good scientist.”Headstrong, as well as Bunting’s predisposition to optimism in Her Two Lives.But how does one bolster or nurture such confidence and boldness? Pollack recounts attending a picnic for the Yale Physics and Astronomy Departments and a conversation with four young women post-docs. They recount how they have depended on the support of each other through the challenges of their training: “If there are enough women in your class, you can help each other get through.” Pollack asked them how they managed to persist (so far) in the pursuit of their dreams of a scientific career when so many others have failed. Their response: “Oh, that’s easy…we’re the women who don’t give a crap…about what people expect us to do. Or not do.” They continued to describe how success, for them, was characterized by a willingness to ignore the unwritten expectations of their field, of their contemporaries, and of society, about what it takes to be a successful woman scientist. While still early in their careers, these post-docs articulated and demonstrated some of the same perseverance and thick-skinned focus as their predecessors did in Pollack also describes the research of Jo Handelsman, published in 2012, that demonstrated how implicit bias still exerts a pervasive influence on how men and women in science are perceived and evaluated. Her team sent curriculum vitae (CVs) to 127 professors of both genders: half received a CV from a man named “John,” and the other half received one from a woman named “Jennifer;” in all other respects, the CVs were identical. Each faculty member rated the candidates on numerous traits including competence, hireability, and likability. The results were striking: irrespective of the respondents’ age, gender, area of specialization, or seniority, the male candidate was rated at least half a point higher in all areas except likability, and the male candidate was offered an average starting salary of US$30,238, compared to the female candidate’s starting salary of US$26,308. The message, says Handelsman, is that implicit bias against women in science is serious, measurable, and so pervasive that it transcends gender, age, and discipline [Blindspot: Hidden Biases of Good People, Mahzarin Banaji and Anthony Greenwald describe their extensive research with a tool designed specifically to detect and help combat implicit biases. The Implicit Association Tool (IAT) is a test that reveals the degree to which our unconscious biases, or “mindbugs,” defined as “imagined habits of thought that lead to errors in how we perceive, remember, reason and make decisions,” affect our perceptions. The authors argue that even if “good people” do not have conscious preferences or biases about many things , they still have empirically observable unconscious biases that influence their attitudes and actions. Even if you don’t read the book, I highly recommend taking some of the IATs available online at https://implicit.harvard.edu/implicit/takeatest.html. I think it is impossible not to be surprised and impressed by the results.This is potentially discouraging: many of the biases demonstrated in Handelsman’s work are implicit, rather than explicit, and therefore harder to identify and mitigate. In the fourth book I am recommending, science = male stereotypes: the greater the stereotype, the greater the gender performance disparity. This, combined with the dramatic decrease in the differences between boys’ and girls’ math and science performances over time, has helped to deflate the ever-persistent stereotype that boys have genetic advantages over girls in these fields and subjects.Banaji and Greenwald describe a study assessing IAT-measured gender-science stereotypes from 34 countries. Their data showed that the national gender differences in eighth-grade boys’ and girls’ math and science achievements was directly correlated with stronger So, what can we do to combat such pervasive mindbugs and biases? The final chapter of the book describes some empirically tested approaches. The authors caution, however, that implicit biases are likely elastic, and, while successful interventions can result in meaningful change, “they will require reapplication prior to each occasion on which one wishes them to be in effect.” The goal should be outsmarting implicit biases, rather than eradicating them.female = family and male = career. Banaji and Greenwald write, “There is no reason to doubt that the mindbugs we direct toward ourselves are every bit as durable as those we direct towards others.” This is the kind of bias that Pollack described experiencing while at Yale in the 1970s and that many of the current students, post-docs, and faculty members that she interviewed worry still persists today.I was especially intrigued by their discussion of self-undermining mindbugs, specifically the observation that not just men but also many women have automatic strong associations of the type Awareness of this unconscious self-undermining is a first step, and, perhaps, even more important when it occurs in the context of a social and institutional climate that reinforces it. However, it is not enough. Banaji and Greenwald describe some empirically proven institutional approaches to minimize this bias among women, including having women college students take math courses taught by female faculty and adding typically feminine décor to computer science classrooms. Telling women to just “believe in themselves” or not to “give a crap” and ignore their own implicit biases as well as those of others is clearly not adequate: institutions and the scientific community itself must find approaches to mitigate and outsmart all of our ongoing implicit biases in this area, all the more now that many explicit biases have been eliminated or diminished.I hope that in the next few years there will be a proliferation of more detailed and nuanced biographies of “scientists who happen to be women,” including more recent stories. Ideally, such biographies would capture the rich depth of their narratives, including their scientific journeys and discoveries, as well as their experiences with the sociology of science, including perceptions of their work and roles as women in science. But I also hope that they include details about how they navigated other aspects of their lives, including partnership, parenthood, and other interests. Furthermore, I hope that additional research will be done documenting external and internal barriers and biases around women in science and the identification of more validated strategies to mitigate them.Swaby R (2015) Headstrong: 52 Women Who Changed Science and the World. New York: Broadway Books.Yaffe E (2005) Mary Ingraham Bunting: Her Two Lives. Savannah: Frederic C Beil.Pollack E (2015) The Only Woman in the Room: Why Science is Still a Boys’ Club. Boston: Beacon Press.Banaji MR, Greenwald AG (2013) Blindspot: Hidden Biases of Good People. New York: Delacorte Press (Available in paperback in August 2016).