| { | |
| "paper_id": "2022", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T06:02:57.529041Z" | |
| }, | |
| "title": "How to be Helpful on Online Support Forums?", | |
| "authors": [ | |
| { | |
| "first": "Zhilin", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of Cambridge and Nvidia", | |
| "location": {} | |
| }, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Pablo", | |
| "middle": [], | |
| "last": "Torres", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of Cambridge", | |
| "location": {} | |
| }, | |
| "email": "" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "Internet forums such as Reddit offer people a platform to ask for advice when they encounter various issues at work, school or in relationships. Telling helpful comments apart from unhelpful comments to these advice-seeking posts can help people and dialogue agents to become more helpful in offering advice. We propose a dataset that contains both helpful and unhelpful comments in response to such requests. We then relate helpfulness to the closely related construct of empathy. Finally, we analyze the language features that are associated with helpful and unhelpful comments.", | |
| "pdf_parse": { | |
| "paper_id": "2022", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "Internet forums such as Reddit offer people a platform to ask for advice when they encounter various issues at work, school or in relationships. Telling helpful comments apart from unhelpful comments to these advice-seeking posts can help people and dialogue agents to become more helpful in offering advice. We propose a dataset that contains both helpful and unhelpful comments in response to such requests. We then relate helpfulness to the closely related construct of empathy. Finally, we analyze the language features that are associated with helpful and unhelpful comments.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [ | |
| { | |
| "text": "When people encounter issues in their lives (such as problems with family and friends, difficulties at school/work as well as troubles in pursuing one's interests and hobbies), many seek for advice in order to solve these problems. Some ask for such advice on internet forums, such as the r/Advice subreddit 1 . Other users can then comment on these posts to attempt to help the post authors.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "While many users can actively offer help, not all of them will be seen as helpful by the user asking for advice. Examples of a helpful and an unhelpful comment are presented in Figure 1 to show their contrast. In order to support people and dialogue agents to be more effective in offering helpful comments, a critical first step is to understand what makes these comments helpful. We make use of a feedback system on r/Advice that labels comments based on whether the original post author finds comments to be helpful. Based on this feedback system, we introduce a new dataset of comments, labelled with their binary helpfulness.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 177, | |
| "end": 185, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Helpfulness has been extensively studied based on exchanges in online support communities (Chuang and Yang, 2012; Schotanus-Dijkstra et al., Figure 1 : Examples of helpful and un-helpful comments to a help-seeking post. \"Helped\" is a magic word for labelling the response.", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 141, | |
| "end": 149, | |
| "text": "Figure 1", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "2014; Paulus and Varga, 2015; Subramani and O'Connor, 2018; McKiernan et al., 2018; Green et al., 2020) . These studies found that helpfulness is associated with various characteristics such as emotional warmth, relevant knowledge, willingness to understand, empowering choice, active listening as well as sharing of similar experiences. However, these studies are solely based on qualitative interpretations and have thus far not sought to associate language features with helpfulness. To overcome this limitation, we seek to identify words that are most positively and negatively associated with helpfulness, and relate these words to characteristics of helpfulness from prior literature.", | |
| "cite_spans": [ | |
| { | |
| "start": 6, | |
| "end": 29, | |
| "text": "Paulus and Varga, 2015;", | |
| "ref_id": "BIBREF13" | |
| }, | |
| { | |
| "start": 30, | |
| "end": 59, | |
| "text": "Subramani and O'Connor, 2018;", | |
| "ref_id": "BIBREF18" | |
| }, | |
| { | |
| "start": 60, | |
| "end": 83, | |
| "text": "McKiernan et al., 2018;", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 84, | |
| "end": 103, | |
| "text": "Green et al., 2020)", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Helpfulness is closely related to empathy, as they share many characteristics such as being emotionally warm and compassionate; accepting others' frame of reference, and practising active listening (Davis, 1983; Baron-Cohen and Wheelwright, 2004; Zhou et al., 2003) . We show that people's average helpfulness across all of their comments correlates with their measured empathy score. We also relate our study to literature on the language features that are associated with empathy (Sharma et al., 2020; Xiao et al., 2015; Gibson et al., 2015) and show that there is a great overlap among their language features.", | |
| "cite_spans": [ | |
| { | |
| "start": 198, | |
| "end": 211, | |
| "text": "(Davis, 1983;", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 212, | |
| "end": 246, | |
| "text": "Baron-Cohen and Wheelwright, 2004;", | |
| "ref_id": "BIBREF0" | |
| }, | |
| { | |
| "start": 247, | |
| "end": 265, | |
| "text": "Zhou et al., 2003)", | |
| "ref_id": "BIBREF23" | |
| }, | |
| { | |
| "start": 482, | |
| "end": 503, | |
| "text": "(Sharma et al., 2020;", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 504, | |
| "end": 522, | |
| "text": "Xiao et al., 2015;", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 523, | |
| "end": 543, | |
| "text": "Gibson et al., 2015)", | |
| "ref_id": "BIBREF5" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Our key contributions are:", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "1. We introduce and plan to openly release a novel dataset containing helpful and unhelpful comments in response to posts seeking for advice on life issues.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "2. We relate helpfulness in comments that respond to posts seeking for advice on life issues to empathy.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "3. We analyze the language features that are associated with helpful and unhelpful comments.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Introduction", | |
| "sec_num": "1" | |
| }, | |
| { | |
| "text": "Helpfulness on Online Support Communities Helpfulness has been studied in online support communities where peers can offer help and support to one another. These communities often center around a shared life situation such as chronic health conditions (Subramani and O'Connor, 2018; Green et al., 2020) and family bereavement (Schotanus-Dijkstra et al., 2014; Paulus and Varga, 2015) . Several factors were emphasized in common: Peers were found more helpful when they are emotionally warm and compassionate, give others choice on a solution, willing to accept others' perspectives and experiences, practice active listeningby paraphrasing, asking questions and reflecting feelings, give pertinent advice/insights to help others to solve their problem, as well as share similar experiences (Chuang and Yang, 2012; Schotanus-Dijkstra et al., 2014; Paulus and Varga, 2015; Subramani and O'Connor, 2018; McKiernan et al., 2018; Green et al., 2020) . While there has been significant work on what people find helpful, existing studies are based on qualitative themes and to the best of our knowledge, no work has been done on the language features that characterizes helpful support messages.", | |
| "cite_spans": [ | |
| { | |
| "start": 252, | |
| "end": 282, | |
| "text": "(Subramani and O'Connor, 2018;", | |
| "ref_id": "BIBREF18" | |
| }, | |
| { | |
| "start": 283, | |
| "end": 302, | |
| "text": "Green et al., 2020)", | |
| "ref_id": "BIBREF6" | |
| }, | |
| { | |
| "start": 326, | |
| "end": 359, | |
| "text": "(Schotanus-Dijkstra et al., 2014;", | |
| "ref_id": "BIBREF16" | |
| }, | |
| { | |
| "start": 360, | |
| "end": 383, | |
| "text": "Paulus and Varga, 2015)", | |
| "ref_id": "BIBREF13" | |
| }, | |
| { | |
| "start": 790, | |
| "end": 813, | |
| "text": "(Chuang and Yang, 2012;", | |
| "ref_id": "BIBREF2" | |
| }, | |
| { | |
| "start": 814, | |
| "end": 846, | |
| "text": "Schotanus-Dijkstra et al., 2014;", | |
| "ref_id": "BIBREF16" | |
| }, | |
| { | |
| "start": 847, | |
| "end": 870, | |
| "text": "Paulus and Varga, 2015;", | |
| "ref_id": "BIBREF13" | |
| }, | |
| { | |
| "start": 871, | |
| "end": 900, | |
| "text": "Subramani and O'Connor, 2018;", | |
| "ref_id": "BIBREF18" | |
| }, | |
| { | |
| "start": 901, | |
| "end": 924, | |
| "text": "McKiernan et al., 2018;", | |
| "ref_id": "BIBREF11" | |
| }, | |
| { | |
| "start": 925, | |
| "end": 944, | |
| "text": "Green et al., 2020)", | |
| "ref_id": "BIBREF6" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Related Work", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "Language Features for Empathy Empathy is closely related to helpfulness, as many factors contributing to helpfulness (being emotionally warm and compassionate; accepting others' perspectives; practising active listening) are also associated with empathy (Davis, 1983; Baron-Cohen and Wheelwright, 2004; Zhou et al., 2003) . There has been significant work on language features that characterize empathy. Sharma et al. (2020) identified that empathy is expressed in language use relating to expressing warm and compassionate emotions, communicating an understanding of others' experience, and asking more about the person's experiences. Xiao et al. (2015) and Gibson et al. (2015) found that language use relating to asking for others' perspective (e.g. it sounds like; do you think) are positively associated with empathy while language use that orders other around (e.g. you need to; please answer the) are negatively associated with empathy. Language features for empathy overlap with the features that characterize helpfulness, reinforcing the strong connection between empathy and helpfulness.", | |
| "cite_spans": [ | |
| { | |
| "start": 254, | |
| "end": 267, | |
| "text": "(Davis, 1983;", | |
| "ref_id": "BIBREF4" | |
| }, | |
| { | |
| "start": 268, | |
| "end": 302, | |
| "text": "Baron-Cohen and Wheelwright, 2004;", | |
| "ref_id": "BIBREF0" | |
| }, | |
| { | |
| "start": 303, | |
| "end": 321, | |
| "text": "Zhou et al., 2003)", | |
| "ref_id": "BIBREF23" | |
| }, | |
| { | |
| "start": 404, | |
| "end": 424, | |
| "text": "Sharma et al. (2020)", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 636, | |
| "end": 654, | |
| "text": "Xiao et al. (2015)", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 659, | |
| "end": 679, | |
| "text": "Gibson et al. (2015)", | |
| "ref_id": "BIBREF5" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Related Work", | |
| "sec_num": "2" | |
| }, | |
| { | |
| "text": "Our English dataset is obtained from r/Advice, which allows post authors to mark out comment(s) that they have found helpful 2 . Comments to posts with at least one helpful comment, but were not themselves labeled as helpful are labelled as unhelpful. This inclusion criterion minimizes the mislabelling of comments to posts whose authors did not actively participate in labelling comments. Text from Reddit was downloaded through the Pushshift Application Programming Interface 3 . Suitable posts and all associated comments from the Advice subreddit were downloaded within 300 days (Apr 2019 -Feb 2020). Comments by the post authors and automated bots were excluded. Across the 24964 posts that were downloaded, there were 92477 associated comments (41146 helpful). On average, each comment has 95.8 words (SD=134.5).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Dataset", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "Training/validation/test split was 80-10-10.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Dataset", | |
| "sec_num": "3" | |
| }, | |
| { | |
| "text": "To determine how helpfulness relates to empathy, we calculate an aggregated metric for each user based on the proportion of their comments found to be helpful. We then correlate average user helpfulness against an established psychological measure of empathy.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "How does Helpfulness Relate to Empathy?", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "Empathy Quotient Questionnaire The short form of Empathy Quotient (EQ) questionnaire (Wakabayashi et al., 2006) was used to measure empathy (details are in appendix A). Higher scores on the EQ represent higher empathy. The EQ questionnaire has high internal consistency (Cronbach's \u03b1 = 0.90) and test-retest reliability after 12 months (r = 0.97, p < .001).", | |
| "cite_spans": [ | |
| { | |
| "start": 85, | |
| "end": 111, | |
| "text": "(Wakabayashi et al., 2006)", | |
| "ref_id": "BIBREF19" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "How does Helpfulness Relate to Empathy?", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "Participants Only users with more than 20 comments were included to minimize the likelihood that their average helpfulness was biased due to limited observations. 508 Reddit users were sent an online questionnaire through Reddit and 91 responded. Gender and age were optional to report. 86 participants reported gender (53 male and 33 female) and 83 reported age (M=33.7, SD=13.8). The mean user helpfulness is 0.5440 (SD=0.1956). Using a two-sample t-test, the distribution of EQ scores (M=24.45, SD=8.822, N=91) in this study is found to be not significantly different (t(1850) = 0.0169, p = 0.9866) from the sample (M=23.8, SD=8.75, N=1761) in Wakabayashi et al. (2006) , demonstrating the representativeness of our sample. Results As illustrated in Figure 2 , there is a moderate correlation effect between EQ and User helpfulness (r(91) = 0.359, p < 0.001). We also explored correlating User helpfulness with various subscales of the EQ, namely cognitive empathy, affective empathy and social skills based on Zhou et al. (2020) . Helpfulness correlates most strongly with cognitive empathy (r(91) = 0.355, p < 0.001), followed by affective empathy (r(91) = 0.261, p = 0.012) and finally social skills (r(91) = 0.203, p = 0.054). This suggests that helpful commenters more often are better able to understand how the post authors think compared to how they feel or communicating it across in a social deft manner (which has a boundary p value).", | |
| "cite_spans": [ | |
| { | |
| "start": 647, | |
| "end": 672, | |
| "text": "Wakabayashi et al. (2006)", | |
| "ref_id": "BIBREF19" | |
| }, | |
| { | |
| "start": 1014, | |
| "end": 1032, | |
| "text": "Zhou et al. (2020)", | |
| "ref_id": "BIBREF22" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 753, | |
| "end": 761, | |
| "text": "Figure 2", | |
| "ref_id": "FIGREF0" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "How does Helpfulness Relate to Empathy?", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "To explore the potential for the dataset to be useful in training models to distinguish between help- ful and unhelpful comments, we trained several baseline models and report their micro-average F1 scores. The performance of baseline models on this task is relatively low but similar to the performance on empathy datasets (Gibson et al., 2015; Khanpour et al., 2017; Sharma et al., 2020) . The relatively low performance of baseline models on this task suggests that while recognizing helpfulness in language is trivial for typically-developing humans, they remain challenging for machines. Techniques such as commonsense reasoning Bosselut et al., 2019) can be explored in the future to better capture the highly complex relationship between language and helpfulness.", | |
| "cite_spans": [ | |
| { | |
| "start": 324, | |
| "end": 345, | |
| "text": "(Gibson et al., 2015;", | |
| "ref_id": "BIBREF5" | |
| }, | |
| { | |
| "start": 346, | |
| "end": 368, | |
| "text": "Khanpour et al., 2017;", | |
| "ref_id": "BIBREF9" | |
| }, | |
| { | |
| "start": 369, | |
| "end": 389, | |
| "text": "Sharma et al., 2020)", | |
| "ref_id": "BIBREF17" | |
| }, | |
| { | |
| "start": 634, | |
| "end": 656, | |
| "text": "Bosselut et al., 2019)", | |
| "ref_id": "BIBREF1" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Predicting for Helpful Comments", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "Significant Predictors of Helpfulness To characterize helpfulness in our dataset, significant predictors of helpfulness (p < 0.05) based on the Logistic Regression model were extracted and analysed. 4 Thematic categories that were inductively generated from these predictors are shown in Table 4 while word clouds are available in Appendix D. The first overarching theme is positive and friendly words. Helpfulness is positively predicted by polite, friendly-sounding and optimisticsounding words but negatively predicted by words that indicate negative emotions. This relates to the literature findings on how uplifting and friendly online support peers are found to be more helpful. (Paulson et al., 1999; Subramani and O'Connor, 2018) Affect-related words (such as sad and tears) were previously found to be significant predictors of empathy (Gibson et al., 2015) .", | |
| "cite_spans": [ | |
| { | |
| "start": 199, | |
| "end": 200, | |
| "text": "4", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 686, | |
| "end": 708, | |
| "text": "(Paulson et al., 1999;", | |
| "ref_id": "BIBREF12" | |
| }, | |
| { | |
| "start": 709, | |
| "end": 738, | |
| "text": "Subramani and O'Connor, 2018)", | |
| "ref_id": "BIBREF18" | |
| }, | |
| { | |
| "start": 846, | |
| "end": 867, | |
| "text": "(Gibson et al., 2015)", | |
| "ref_id": "BIBREF5" | |
| } | |
| ], | |
| "ref_spans": [ | |
| { | |
| "start": 288, | |
| "end": 296, | |
| "text": "Table 4", | |
| "ref_id": "TABREF7" | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Predicting for Helpful Comments", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "A second overarching theme is words relating to attempts to understand the perspective of others. Helpful commenters do so by addressing post authors directly, instead of patronizing the difficulties that they face. This is also in agreement with Green et al., 2020) Furthermore, terms indicating an inclination to find out more about the perspective of others (e.g. \"do you think\", \"it sounds like\" and \"you think about\") were also predictors in empathy datasets (Gibson et al., 2015; Xiao et al., 2015) . Overall, the overarching themes that are predictive of helpfulness in our dataset are supported by literature on helpfulness and language features associated with empathy.", | |
| "cite_spans": [ | |
| { | |
| "start": 464, | |
| "end": 485, | |
| "text": "(Gibson et al., 2015;", | |
| "ref_id": "BIBREF5" | |
| }, | |
| { | |
| "start": 486, | |
| "end": 504, | |
| "text": "Xiao et al., 2015)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Predicting for Helpful Comments", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "To better understand the capabilities and limitations of language features in capturing comment helpfulness, two graduate students manually annotated a selection of helpful comments. Annotations were done on 5 comments each from 91 authors who responded to our empathy quotient questionnaire. Comments were sampled using a stratified approach that results in a sampled average helpfulness to be closest possible to the author's average helpfulness score (P earson s r = 0.937, p < 0.001).", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Human-Annotated Features for Comment Helpfulness", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "Then we labelled each comment with one or more of the 10 possible labels based on helpfulness literature (see Section 2). They are 1. Highly directive, short advice 2. Dismissing concern 3. Negative terms 4. Tangential or unspecific comment 5. Share similar experience 6. Ask clarifying questions 7.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Human-Annotated Features for Comment Helpfulness", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "Relevant knowledge 8. Emotional support 9. Recognizing difficulty 10. Tentative language. Average Cohen's \u03ba is 0.690 (\u03c3=0.107). Definitions and Cohen's \u03ba for each label are in Appendix 5. Using a logistic regression, we found that only the use of negative terms and tangential or unspecific comment are negatively associated with helpfulness (p < 0.05) while providing relevant knowledge is positively associated (p < 0.05). The use of negative terms was also captured by the logistic regression based on language use while the other two factors were not. An inspection of examples revealed that negative terms only comprises of a small set of words while those two factors require contextual semantic understanding of what is relevant knowledge to a situation and what is tangential. Future work can make use of knowledge-enhanced models (Peters et al., 2019; Clark et al., 2021) to better capture such contextual understanding.", | |
| "cite_spans": [ | |
| { | |
| "start": 839, | |
| "end": 860, | |
| "text": "(Peters et al., 2019;", | |
| "ref_id": "BIBREF14" | |
| }, | |
| { | |
| "start": 861, | |
| "end": 880, | |
| "text": "Clark et al., 2021)", | |
| "ref_id": "BIBREF3" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Human-Annotated Features for Comment Helpfulness", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "We introduce and plan to openly release a novel dataset containing helpful and unhelpful comments in response to posts seeking for advice on life issues. We show that the helpfulness of such comments is related to the commenters' empathy and pioneer an analysis into language features predictive of helpful and unhelpful comments on online support communities. Our work can contribute towards supporting people and automated dialogue agents to offer more helpful comments to others.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": "7" | |
| }, | |
| { | |
| "text": "This project has been approved by University of Cambridge Faculty of Education Institutional Review Board. The use of Reddit data in this project is in alignment with the Reddit End User License Agreement and the Terms of Use for Developers. Because part of the project requires participants to respond to questionnaires, we made sure that the items were phrased sensitively so that no unintended harm would be caused. No payment was made to voluntary participants, as the survey could be done within a few minutes. We also guided participants to make informed decisions about their participation, giving them the opportunity to withdraw any time, during and after the completion of the questionnaire. The collected information, which does not include personally identifiable information, was stored securely with access restricted to the research team. We also manually inspected a small selection of Reddit data to ensure that they do not contain names, personally identifying information or offensive content. We anticipate that this project can accelerate the development of models that can better detect and express helpfulness in social settings, between humans and with social dialogue agents.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Ethics and Broader Impact", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Items originate from the long form of Empathy Quotient questionnaire (Baron-Cohen and Wheelwright, 2004) , which is well-cited (>3500 citations) and demonstrates good validity in large (>500,000) and culturally-diverse samples (Kosonogov, 2014; Groen et al., 2015; Greenberg et al., 2018) . The short form was chosen to reduce the time taken to answer the questionnaire and thereby increase the response rate. The short form is a 22-item forced-choice self-report questionnaire that can be answered on a four-point Likert Scale (Strongly Agree, Agree, Disagree, Strongly Disagree). Questions include \"I often find it difficult to judge if something is rude or polite\", \"I can pick up quickly if someone says one thing but means another\", and \"I am good at predicting how someone will feel\". Each response can give 0, 1 or 2 points, leading to a maximum total EQ score of 44.", | |
| "cite_spans": [ | |
| { | |
| "start": 86, | |
| "end": 104, | |
| "text": "Wheelwright, 2004)", | |
| "ref_id": "BIBREF0" | |
| }, | |
| { | |
| "start": 227, | |
| "end": 244, | |
| "text": "(Kosonogov, 2014;", | |
| "ref_id": "BIBREF10" | |
| }, | |
| { | |
| "start": 245, | |
| "end": 264, | |
| "text": "Groen et al., 2015;", | |
| "ref_id": "BIBREF8" | |
| }, | |
| { | |
| "start": 265, | |
| "end": 288, | |
| "text": "Greenberg et al., 2018)", | |
| "ref_id": "BIBREF7" | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "A Empathy Quotient Questionnaire", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Each model was run with 5 different random seeds.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "B Baseline Models", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "BERT Pre-trained BERT English-base-uncased WordPiece tokenizer was used. We fine-tuned a BERT Sequence Prediction model (Englishbase-uncased version with 12-layer, 768-hidden, 12-heads, 110M parameters accessed from https://github.com/huggingface/ transformers). BertAdam optimizer was used with 0.1 epoch for warmup and learning rate of 2 * 10 \u22126 following a search within {1,2,5} * 10 n , \u22126 \u2265 n \u2265 \u22124 using F1 as criterion. Maximum sequence length was 512 tokens, batch-size was 8 and epoch number was 2. Training took 4 hours on a Nvidia P100 GPU.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "B Baseline Models", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Others Text was split up into individual words and lower-cased. The number of times each word occurred in each text was then counted. Words that occurred fewer than ten times altogether were removed to minimize the effects of misspelled or rare words. Logistic Regression, Linear Support Vector Classifier, Multinomial Naive Bayes and Random Forest models were trained (accessed from https: //scikit-learn.org/stable/) All hyperparameters were default except adjusting the number of estimators in the Random Forest model to 100. Training took negligible time (< 0.5 hours) on CPU. ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "B Baseline Models", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Size of words are directly proportional to their significance of correlation. Saying that what the post author is going through is not a big deal 0.662 Negative terms", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "D Word Clouds of Significant Predictors of Helpfulness", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Mentioning negative terms that the author did not bring up 0.788 (crazy, psycho etc). Tangential or unspecific comment Mentioning random terms that has nothing to do with the author's post. 0.794 Share similar experience Bringing up that the comment author experienced something 0.677 similar as the post author Ask clarifying questions Asking questions to clarify what the author's situation really is. 0.644 Alternatively, they can be saying \"If it's situation A then . . . , otherwise if situation B then . . . \"", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "D Word Clouds of Significant Predictors of Helpfulness", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Bringing any knowledge to help solve the post author's specific situation 0.802 (for instance, something like \"you can try . . . \" or \"there is this resource . . . \") Emotional support", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Relevant knowledge", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Offering emotional comfort to the post author (something like 0.650 I am sure this will get better or It's definitely not your fault)", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Relevant knowledge", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "Acknowledging that it's a very bad situation for the author to be in 0.419* (I'm sorry that this is a really bad situation) Tentative language Phrasing advice as tentative suggestions -such as using 0.739 \"you might want to try . . . \" or \" I am no expert on this but . . . \" Table 5 : Labels, descriptions and Cohen's \u03ba for manual annotation of helpfulness in comments. * Cohen's \u03ba for \"Recognizing difficulty\" is low due to the very low number of positive labels (<5%) Figure 5 : Empathy questionnaire instructions", | |
| "cite_spans": [], | |
| "ref_spans": [ | |
| { | |
| "start": 276, | |
| "end": 283, | |
| "text": "Table 5", | |
| "ref_id": null | |
| }, | |
| { | |
| "start": 471, | |
| "end": 479, | |
| "text": "Figure 5", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Recognizing difficulty", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "https://www.reddit.com/r/Advice/", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "This is done using the magic word \"helped\", which is picked up by AdviceFlairBot 3 https://pushshift.io/", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| }, | |
| { | |
| "text": "The dataset used to extract the most significant predictors is slightly different. Only one comment was sampled from each post and author to overcome the problem that the covariance matrix was originally non-invertible.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
| "bib_entries": { | |
| "BIBREF0": { | |
| "ref_id": "b0", | |
| "title": "The empathy quotient: An investigation of adults with asperger syndrome or high functioning autism, and normal sex differences", | |
| "authors": [ | |
| { | |
| "first": "Simon", | |
| "middle": [], | |
| "last": "Baron-Cohen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Sally", | |
| "middle": [], | |
| "last": "Wheelwright", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2004, | |
| "venue": "Journal of Autism and Developmental Disorders", | |
| "volume": "34", | |
| "issue": "2", | |
| "pages": "163--175", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1023/b:jadd.0000022607.19833.00" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Simon Baron-Cohen and Sally Wheelwright. 2004. The empathy quotient: An investigation of adults with asperger syndrome or high functioning autism, and normal sex differences. Journal of Autism and Developmental Disorders, 34(2):163-175.", | |
| "links": null | |
| }, | |
| "BIBREF1": { | |
| "ref_id": "b1", | |
| "title": "Comet: Commonsense transformers for automatic knowledge graph construction", | |
| "authors": [ | |
| { | |
| "first": "Antoine", | |
| "middle": [], | |
| "last": "Bosselut", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hannah", | |
| "middle": [], | |
| "last": "Rashkin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Maarten", | |
| "middle": [], | |
| "last": "Sap", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chaitanya", | |
| "middle": [], | |
| "last": "Malaviya", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Asli", | |
| "middle": [], | |
| "last": "Celikyilmaz", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yejin", | |
| "middle": [], | |
| "last": "Choi", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "ACL", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chai- tanya Malaviya, Asli Celikyilmaz, and Yejin Choi. 2019. Comet: Commonsense transformers for auto- matic knowledge graph construction. In ACL.", | |
| "links": null | |
| }, | |
| "BIBREF2": { | |
| "ref_id": "b2", | |
| "title": "Interaction patterns of nurturant support exchanged in online health social networking", | |
| "authors": [ | |
| { | |
| "first": "Y", | |
| "middle": [], | |
| "last": "Katherine", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Christopher C", | |
| "middle": [], | |
| "last": "Chuang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Yang", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2012, | |
| "venue": "", | |
| "volume": "14", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.2196/jmir.1824" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Katherine Y Chuang and Christopher C Yang. 2012. In- teraction patterns of nurturant support exchanged in online health social networking. 14(3):e54.", | |
| "links": null | |
| }, | |
| "BIBREF3": { | |
| "ref_id": "b3", | |
| "title": "Integrating transformers and knowledge graphs for Twitter stance detection", | |
| "authors": [ | |
| { | |
| "first": "Thomas", | |
| "middle": [], | |
| "last": "Clark", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Costanza", | |
| "middle": [], | |
| "last": "Conforti", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Fangyu", | |
| "middle": [], | |
| "last": "Liu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Zaiqiao", | |
| "middle": [], | |
| "last": "Meng", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ehsan", | |
| "middle": [], | |
| "last": "Shareghi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Nigel", | |
| "middle": [], | |
| "last": "Collier", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2021, | |
| "venue": "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "304--312", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Thomas Clark, Costanza Conforti, Fangyu Liu, Zaiqiao Meng, Ehsan Shareghi, and Nigel Collier. 2021. In- tegrating transformers and knowledge graphs for Twitter stance detection. In Proceedings of the Sev- enth Workshop on Noisy User-generated Text (W- NUT 2021), pages 304-312, Online. Association for Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF4": { | |
| "ref_id": "b4", | |
| "title": "Measuring individual differences in empathy: Evidence for a multidimensional approach", | |
| "authors": [ | |
| { | |
| "first": "H", | |
| "middle": [], | |
| "last": "Mark", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Davis", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1983, | |
| "venue": "Journal of Personality and Social Psychology", | |
| "volume": "44", | |
| "issue": "1", | |
| "pages": "113--126", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1037/0022-3514.44.1.113" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Mark H. Davis. 1983. Measuring individual differ- ences in empathy: Evidence for a multidimensional approach. Journal of Personality and Social Psy- chology, 44(1):113-126.", | |
| "links": null | |
| }, | |
| "BIBREF5": { | |
| "ref_id": "b5", | |
| "title": "Predicting therapist empathy in motivational interviews using language features inspired by psycholinguistic norms", | |
| "authors": [ | |
| { | |
| "first": "James", | |
| "middle": [], | |
| "last": "Gibson", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Nikolaos", | |
| "middle": [], | |
| "last": "Malandrakis", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Francisco", | |
| "middle": [], | |
| "last": "Romero", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Atkins", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Shrikanth", | |
| "middle": [ | |
| "S" | |
| ], | |
| "last": "Narayanan", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Proceedings of Interspeech", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "James Gibson, Nikolaos Malandrakis, Francisco Romero, David Atkins, and Shrikanth S. Narayanan. 2015. Predicting therapist empathy in motivational interviews using language features inspired by psy- cholinguistic norms. In Proceedings of Interspeech.", | |
| "links": null | |
| }, | |
| "BIBREF6": { | |
| "ref_id": "b6", | |
| "title": "Assessment of adaptive engagement and support model for people with chronic health conditions in online health communities: Combined content analysis", | |
| "authors": [ | |
| { | |
| "first": "Katelyn", | |
| "middle": [ | |
| "Tente" | |
| ], | |
| "last": "Brian M Green", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ketki", | |
| "middle": [], | |
| "last": "Van Horn", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Megan", | |
| "middle": [], | |
| "last": "Gupte", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Sara", | |
| "middle": [], | |
| "last": "Evans", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Amrita", | |
| "middle": [], | |
| "last": "Hayes", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Bhowmick", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2020, | |
| "venue": "", | |
| "volume": "22", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.2196/17338" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Brian M Green, Katelyn Tente Van Horn, Ketki Gupte, Megan Evans, Sara Hayes, and Amrita Bhowmick. 2020. Assessment of adaptive engagement and sup- port model for people with chronic health conditions in online health communities: Combined content analysis. 22(7):e17338.", | |
| "links": null | |
| }, | |
| "BIBREF7": { | |
| "ref_id": "b7", | |
| "title": "Testing the empathizing-systemizing theory of sex differences and the extreme male brain theory of autism in half a million people", | |
| "authors": [ | |
| { | |
| "first": "David", | |
| "middle": [ | |
| "M" | |
| ], | |
| "last": "Greenberg", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Varun", | |
| "middle": [], | |
| "last": "Warrier", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Carrie", | |
| "middle": [], | |
| "last": "Allison", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Simon", | |
| "middle": [], | |
| "last": "Baron-Cohen", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "Proceedings of the National Academy of Sciences", | |
| "volume": "115", | |
| "issue": "48", | |
| "pages": "12152--12157", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1073/pnas.1811032115" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "David M. Greenberg, Varun Warrier, Carrie Allison, and Simon Baron-Cohen. 2018. Testing the em- pathizing-systemizing theory of sex differences and the extreme male brain theory of autism in half a mil- lion people. Proceedings of the National Academy of Sciences, 115(48):12152-12157.", | |
| "links": null | |
| }, | |
| "BIBREF8": { | |
| "ref_id": "b8", | |
| "title": "The empathy and systemizing quotient: The psychometric properties of the dutch version and a review of the crosscultural stability", | |
| "authors": [ | |
| { | |
| "first": "Y", | |
| "middle": [], | |
| "last": "Groen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [ | |
| "B M" | |
| ], | |
| "last": "Fuermaier", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "A", | |
| "middle": [ | |
| "E" | |
| ], | |
| "last": "Heijer", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "O", | |
| "middle": [], | |
| "last": "Tucha", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "M", | |
| "middle": [], | |
| "last": "Althaus", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "Journal of Autism and Developmental Disorders", | |
| "volume": "45", | |
| "issue": "9", | |
| "pages": "2848--2864", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1007/s10803-015-2448-z" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Y. Groen, A. B. M. Fuermaier, A. E. Den Heijer, O. Tucha, and M. Althaus. 2015. The empathy and systemizing quotient: The psychometric prop- erties of the dutch version and a review of the cross- cultural stability. Journal of Autism and Develop- mental Disorders, 45(9):2848-2864.", | |
| "links": null | |
| }, | |
| "BIBREF9": { | |
| "ref_id": "b9", | |
| "title": "Identifying empathetic messages in online health communities", | |
| "authors": [ | |
| { | |
| "first": "Hamed", | |
| "middle": [], | |
| "last": "Khanpour", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Cornelia", | |
| "middle": [], | |
| "last": "Caragea", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Prakhar", | |
| "middle": [], | |
| "last": "Biyani", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2017, | |
| "venue": "Proceedings of the Eighth International Joint Conference on Natural Language Processing", | |
| "volume": "2", | |
| "issue": "", | |
| "pages": "246--251", | |
| "other_ids": {}, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Hamed Khanpour, Cornelia Caragea, and Prakhar Biyani. 2017. Identifying empathetic messages in online health communities. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 246-251, Taipei, Taiwan. Asian Federation of Natural Language Processing.", | |
| "links": null | |
| }, | |
| "BIBREF10": { | |
| "ref_id": "b10", | |
| "title": "The psychometric properties of the russian version of the empathy quotient", | |
| "authors": [ | |
| { | |
| "first": "Vladimir", | |
| "middle": [], | |
| "last": "Kosonogov", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "Psychology in Russia: State of the Art", | |
| "volume": "7", | |
| "issue": "", | |
| "pages": "96--104", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.11621/pir.2014.0110" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Vladimir Kosonogov. 2014. The psychometric proper- ties of the russian version of the empathy quotient. Psychology in Russia: State of the Art, 7:96-104.", | |
| "links": null | |
| }, | |
| "BIBREF11": { | |
| "ref_id": "b11", | |
| "title": "Understanding young people's relationship breakups using the dual processing model of coping and bereavement", | |
| "authors": [ | |
| { | |
| "first": "Aidan", | |
| "middle": [], | |
| "last": "Mckiernan", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Patrick", | |
| "middle": [], | |
| "last": "Ryan", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Eimear", | |
| "middle": [], | |
| "last": "Mcmahon", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Stephen", | |
| "middle": [], | |
| "last": "Bradley", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Ellen", | |
| "middle": [], | |
| "last": "Butler", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "", | |
| "volume": "23", | |
| "issue": "", | |
| "pages": "192--210", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1080/15325024.2018.1426979" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Aidan McKiernan, Patrick Ryan, Eimear McMahon, Stephen Bradley, and Ellen Butler. 2018. Under- standing young people's relationship breakups using the dual processing model of coping and bereave- ment. 23(3):192-210.", | |
| "links": null | |
| }, | |
| "BIBREF12": { | |
| "ref_id": "b12", | |
| "title": "Clients' perceptions of helpful experiences in counseling", | |
| "authors": [ | |
| { | |
| "first": "Barbara", | |
| "middle": [ | |
| "L" | |
| ], | |
| "last": "Paulson", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Derek", | |
| "middle": [], | |
| "last": "Truscott", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Janice", | |
| "middle": [], | |
| "last": "Stuart", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 1999, | |
| "venue": "", | |
| "volume": "46", | |
| "issue": "", | |
| "pages": "317--324", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1037/0022-0167.46.3.317" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Barbara L. Paulson, Derek Truscott, and Janice Stuart. 1999. Clients' perceptions of helpful experiences in counseling. 46(3):317-324.", | |
| "links": null | |
| }, | |
| "BIBREF13": { | |
| "ref_id": "b13", | |
| "title": "please know that you are not alone with your pain\": Responses to newcomer posts in an online grief support forum", | |
| "authors": [ | |
| { | |
| "first": "Trena", | |
| "middle": [ | |
| "M" | |
| ], | |
| "last": "Paulus", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mary", | |
| "middle": [ | |
| "Alice" | |
| ], | |
| "last": "Varga", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "", | |
| "volume": "39", | |
| "issue": "", | |
| "pages": "633--640", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1080/07481187.2015.1047060" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Trena M. Paulus and Mary Alice Varga. 2015. \"please know that you are not alone with your pain\": Re- sponses to newcomer posts in an online grief support forum. 39(10):633-640.", | |
| "links": null | |
| }, | |
| "BIBREF14": { | |
| "ref_id": "b14", | |
| "title": "Knowledge enhanced contextual word representations", | |
| "authors": [ | |
| { | |
| "first": "Matthew", | |
| "middle": [ | |
| "E" | |
| ], | |
| "last": "Peters", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Mark", | |
| "middle": [], | |
| "last": "Neumann", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Robert", | |
| "middle": [], | |
| "last": "Logan", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Roy", | |
| "middle": [], | |
| "last": "Schwartz", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Vidur", | |
| "middle": [], | |
| "last": "Joshi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Sameer", | |
| "middle": [], | |
| "last": "Singh", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Noah", | |
| "middle": [ | |
| "A" | |
| ], | |
| "last": "Smith", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "43--54", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.18653/v1/D19-1005" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Matthew E. Peters, Mark Neumann, Robert Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A. Smith. 2019. Knowledge enhanced contextual word representations. In Proceedings of the 2019 Con- ference on Empirical Methods in Natural Language Processing and the 9th International Joint Confer- ence on Natural Language Processing (EMNLP- IJCNLP), pages 43-54, Hong Kong, China. Associ- ation for Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF15": { | |
| "ref_id": "b15", | |
| "title": "Atomic: An atlas of machine commonsense for ifthen reasoning", | |
| "authors": [ | |
| { | |
| "first": "Maarten", | |
| "middle": [], | |
| "last": "Sap", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Emily", | |
| "middle": [], | |
| "last": "Ronan Le Bras", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Chandra", | |
| "middle": [], | |
| "last": "Allaway", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Nicholas", | |
| "middle": [], | |
| "last": "Bhagavatula", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Hannah", | |
| "middle": [], | |
| "last": "Lourie", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Brendan", | |
| "middle": [], | |
| "last": "Rashkin", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Noah", | |
| "middle": [ | |
| "A" | |
| ], | |
| "last": "Roof", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Yejin", | |
| "middle": [], | |
| "last": "Smith", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Choi", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2019, | |
| "venue": "Proceedings of the AAAI Conference on Artificial Intelligence", | |
| "volume": "33", | |
| "issue": "", | |
| "pages": "3027--3035", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1609/aaai.v33i01.33013027" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Maarten Sap, Ronan Le Bras, Emily Allaway, Chan- dra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, and Yejin Choi. 2019. Atomic: An atlas of machine commonsense for if- then reasoning. Proceedings of the AAAI Confer- ence on Artificial Intelligence, 33(01):3027-3035.", | |
| "links": null | |
| }, | |
| "BIBREF16": { | |
| "ref_id": "b16", | |
| "title": "What do the bereaved by suicide communicate in online support groups?", | |
| "authors": [ | |
| { | |
| "first": "Marijke", | |
| "middle": [], | |
| "last": "Schotanus-Dijkstra", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Petra", | |
| "middle": [], | |
| "last": "Havinga", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Lynn", | |
| "middle": [], | |
| "last": "Wouter Van Ballegooijen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Delfosse", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2014, | |
| "venue": "", | |
| "volume": "35", | |
| "issue": "", | |
| "pages": "27--35", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1027/0227-5910/a000225" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Marijke Schotanus-Dijkstra, Petra Havinga, Wouter van Ballegooijen, Lynn Delfosse, Jan Mokkenstorm, and Brigitte Boon. 2014. What do the bereaved by suicide communicate in online support groups? 35(1):27-35.", | |
| "links": null | |
| }, | |
| "BIBREF17": { | |
| "ref_id": "b17", | |
| "title": "A computational approach to understanding empathy expressed in text-based mental health support", | |
| "authors": [ | |
| { | |
| "first": "Ashish", | |
| "middle": [], | |
| "last": "Sharma", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Adam", | |
| "middle": [], | |
| "last": "Miner", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "David", | |
| "middle": [], | |
| "last": "Atkins", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Tim", | |
| "middle": [], | |
| "last": "Althoff", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2020, | |
| "venue": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "5263--5276", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.18653/v1/2020.emnlp-main.425" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ashish Sharma, Adam Miner, David Atkins, and Tim Althoff. 2020. A computational approach to un- derstanding empathy expressed in text-based men- tal health support. In Proceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 5263-5276, Online. As- sociation for Computational Linguistics.", | |
| "links": null | |
| }, | |
| "BIBREF18": { | |
| "ref_id": "b18", | |
| "title": "Extracting actionable knowledge from domestic violence discourses on social media", | |
| "authors": [ | |
| { | |
| "first": "Sudha", | |
| "middle": [], | |
| "last": "Subramani", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "O'", | |
| "middle": [], | |
| "last": "Manjula", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Connor", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2018, | |
| "venue": "", | |
| "volume": "5", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.4108/eai.29-5-2018.154807" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Sudha Subramani and Manjula O'Connor. 2018. Ex- tracting actionable knowledge from domestic vio- lence discourses on social media. 5(17):154807.", | |
| "links": null | |
| }, | |
| "BIBREF19": { | |
| "ref_id": "b19", | |
| "title": "Development of short forms of the empathy quotient (EQshort) and the systemizing quotient (SQ-short). Personality and Individual Differences", | |
| "authors": [ | |
| { | |
| "first": "Akio", | |
| "middle": [], | |
| "last": "Wakabayashi", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Simon", | |
| "middle": [], | |
| "last": "Baron-Cohen", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Sally", | |
| "middle": [], | |
| "last": "Wheelwright", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Nigel", | |
| "middle": [], | |
| "last": "Goldenfeld", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Joe", | |
| "middle": [], | |
| "last": "Delaney", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Debra", | |
| "middle": [], | |
| "last": "Fine", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Richard", | |
| "middle": [], | |
| "last": "Smith", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Leonora", | |
| "middle": [], | |
| "last": "Weil", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2006, | |
| "venue": "", | |
| "volume": "41", | |
| "issue": "", | |
| "pages": "929--940", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1016/j.paid.2006.03.017" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Akio Wakabayashi, Simon Baron-Cohen, Sally Wheel- wright, Nigel Goldenfeld, Joe Delaney, Debra Fine, Richard Smith, and Leonora Weil. 2006. Develop- ment of short forms of the empathy quotient (EQ- short) and the systemizing quotient (SQ-short). Per- sonality and Individual Differences, 41(5):929-940.", | |
| "links": null | |
| }, | |
| "BIBREF21": { | |
| "ref_id": "b21", | |
| "title": "rate my therapist\": Automated detection of empathy in drug and alcohol counseling via speech and language processing", | |
| "authors": [ | |
| { | |
| "first": "Shrikanth", | |
| "middle": [ | |
| "S" | |
| ], | |
| "last": "Atkins", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Narayanan", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2015, | |
| "venue": "PLOS ONE", | |
| "volume": "10", | |
| "issue": "12", | |
| "pages": "1--15", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1371/journal.pone.0143055" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Atkins, and Shrikanth S. Narayanan. 2015. \"rate my therapist\": Automated detection of empathy in drug and alcohol counseling via speech and language pro- cessing. PLOS ONE, 10(12):1-15.", | |
| "links": null | |
| }, | |
| "BIBREF22": { | |
| "ref_id": "b22", | |
| "title": "Psychometric properties of the chinese empathy and systemizing quotients in a non", | |
| "authors": [ | |
| { | |
| "first": "Ningning", | |
| "middle": [], | |
| "last": "Zhou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Danni", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Gregory", | |
| "middle": [ | |
| "S" | |
| ], | |
| "last": "Chasson", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Xin", | |
| "middle": [], | |
| "last": "Xu", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Jianping", | |
| "middle": [], | |
| "last": "Wang", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Maria", | |
| "middle": [ | |
| "Izabel" | |
| ], | |
| "last": "Lockwood", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2020, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1007/s12144-020-00653-z" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Ningning Zhou, Danni Wang, Gregory S. Chasson, Xin Xu, Jianping Wang, and Maria Izabel Lockwood. 2020. Psychometric properties of the chinese empa- thy and systemizing quotients in a non-clinical sam- ple.", | |
| "links": null | |
| }, | |
| "BIBREF23": { | |
| "ref_id": "b23", | |
| "title": "Positive psychological assessment: A handbook of models and measures", | |
| "authors": [ | |
| { | |
| "first": "Qing", | |
| "middle": [], | |
| "last": "Zhou", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Carlos", | |
| "middle": [], | |
| "last": "Valiente", | |
| "suffix": "" | |
| }, | |
| { | |
| "first": "Nancy", | |
| "middle": [], | |
| "last": "Eisenberg", | |
| "suffix": "" | |
| } | |
| ], | |
| "year": 2003, | |
| "venue": "", | |
| "volume": "", | |
| "issue": "", | |
| "pages": "269--284", | |
| "other_ids": { | |
| "DOI": [ | |
| "10.1037/10612-017" | |
| ] | |
| }, | |
| "num": null, | |
| "urls": [], | |
| "raw_text": "Qing Zhou, Carlos Valiente, and Nancy Eisenberg. 2003. Empathy and its measurement. Positive psy- chological assessment: A handbook of models and measures., page 269-284.", | |
| "links": null | |
| } | |
| }, | |
| "ref_entries": { | |
| "FIGREF0": { | |
| "type_str": "figure", | |
| "text": "Empathy quotient (EQ) score against User Helpfulness", | |
| "num": null, | |
| "uris": null | |
| }, | |
| "FIGREF1": { | |
| "type_str": "figure", | |
| "text": "Significant positive predictors of helpfulness Significant negative predictors o helpfulness", | |
| "num": null, | |
| "uris": null | |
| }, | |
| "TABREF1": { | |
| "type_str": "table", | |
| "content": "<table/>", | |
| "html": null, | |
| "text": "Performance of baseline models on test set. Details of their preparation are in Appendix B", | |
| "num": null | |
| }, | |
| "TABREF3": { | |
| "type_str": "table", | |
| "content": "<table/>", | |
| "html": null, | |
| "text": "", | |
| "num": null | |
| }, | |
| "TABREF5": { | |
| "type_str": "table", | |
| "content": "<table/>", | |
| "html": null, | |
| "text": "Performance of baseline models on validation set.", | |
| "num": null | |
| }, | |
| "TABREF7": { | |
| "type_str": "table", | |
| "content": "<table><tr><td>Label</td><td>Description</td></tr></table>", | |
| "html": null, | |
| "text": "Statistical Description for themes identified from significantly predictors of Helpfulness. Helpful comments contain more words from both positive and negative predictors, but the gap between helpful and unhelpful comments is greater for positive predictors.", | |
| "num": null | |
| } | |
| } | |
| } | |
| } |