What Snippets Feel: Depression, Search, and Snippets Ashlee Milton Maria Soledad Pera ashleemilton@u.boisestate.edu solepera@boisestate.edu PIReT – People and Information Research Team PIReT – People and Information Research Team Boise State University, Boise, Idaho, USA Boise State University, Boise, Idaho, USA ABSTRACT Mental health disorders (MHD) is a rising, yet stigmatized, topic in the United States. Individuals suffering from MHD are slowing starting to overcome this stigma by discussing how technology affects them. Researchers have explored behavioral nuances that emerge from interactions of individuals affected by MHD with persuasive technologies, mainly social media. Yet, there is a gap in the analysis pertaining to search engines, another persuasive technology, which is part of their everyday lives. In this paper, we report the results of an initial exploratory analysis conducted to understand the sentiment/emotion profiles of search engines handling the information needs of searchers with MHD. CCS CONCEPTS • Information systems → Sentiment analysis. KEYWORDS Mental Health, Search Engines, Snippets, Emotion 1 INTRODUCTION Persuasive technologies can change the behaviors or attitudes of individuals [9], but not all people are affected in the same manner. Consider people suffering from mental health disorders (MHD). Figure 1: Google’s SERP for the query "waste of space" They tend to be more sensitive or easily influenced, making it natu- turning to SE regularly [1, 5], SE users affected by MHD become a ral to think they would interact with and be affected by persuasive large population, which makes it imperative to explore how this technologies in different manner than average individuals. Mental diverse group of users interact with and are effected by SE, so we illness is a rising issue in modern society. Over the last couple of may better support their online quests for information. years people have started to more openly discuss mental illness To set the ground work necessary to address this important and how it effects their lives [12]. As this discussion continues, it need, we focus our initial exploration on search engine results leads to the question: are there consequences that can unknowingly pages (SERP) and feelings. Specifically, we examine the feelings occur as a result of individuals suffering from MHD engaging with that emerge from SERP, as they present a first impression of the persuasive technologies? type of resources users will be exposed to, in response to their Search engines (SE) are an example of an ubiquitous persuasive queries. To do so, we follow the framework set forth by Kazai et al. technology. Yet, there is very little information regarding how users [11] for exploring the sentiment/emotion profiles of SE responding with MHD interact or are effected by resources accessed via SE. to queries crafted by traditional searchers. In our case, we compare Consider Figure 1, which captures a snapshot of snippets generated and contrast profiles generated by examining snippets of resources by Google for the query "waste of space", a phrase a person with retrieved in response to inquires associated to individuals with depression may say. Among the resources retrieved we see mostly MHD. Given that the interactions of people with MHD and SE dictionaries, which on the surface seem pretty benign. However are not available for analysis, we built a synthetic query log that when examining the snippets of the resources we see phrases like, mimics these interactions. For this purpose, we use posts from "worthless person", "He’s a complete wast of space", "fat bastard", Reddit. Given that we take advantage of this plarform to create "goddam wast of space", and "I’m just a waste of space". Now imag- our synthetic queries and that the age range for 90% of Reddit ine being someone battling a MHD like depression, how do you users is 18-49, the center of attention for our analysis are adults. think being exposed to these phrases would effect you? As reported Further, we posit that the degree to which sentiments/emotions by the National Institute of Mental Health, 1 in 5 adults in the effect users with MHD depends on the kind of MHD they have. united states suffer from a MHD [13]. With millions of individuals Thus, to narrow scope and not overgeneralize, we only explore "Copyright © 2020 for this paper by its authors. Use permitted under Creative Com- depression and anxiety. In fact, from here on, when we use MHD mons License Attribution 4.0 International (CC BY 4.0)." we only refer to depression and anxiety. We describe below the empirical analysis we conducted to un- In creating synthetic queries from searchers with MHD, we turn derstand the intensity in which feelings are present in snippets to Reddit, since its posts capture the language and topics used by responding non-traditional users. Outcomes from this analysis will our target population in an online forum environment, and it has act as a springboard for future work in SE design to support indi- been used in other MHD studies, but in the social media domain [7]. viduals affected by diverse MHD across ages. Specifically, we use 2,600 posts extracted from 10 subreddits, includ- ing r/getting_over_it, r/depression, and r/suicidewatch. We selected 2 RELATED WORK these subreddits as they have users that self-identify as having From a persuasive technology perspective, MHD literature is fo- MHD. From the aforementioned posts we extract the 4,418 most cused on the social media domain. Mainly, the ability to identify frequent n-grams (1 ≤ 𝑛 ≤ 4), using NLTK noun phrase chunking, users with depression from social media posts [6, 7, 14, 15, 18]. which we use as synthetic queries for MQl.Topics and language Depressed users have not been the only ones considered, as re- across subreddits related to MHD greatly vary. Thus, we grouped searchers have also studied linguistics of social media posts by pairs in MQl into 3 categories based on the levels users with other MHD, such as schizophrenia [3]. Some of the most of severity of MHD [13, 25]: (1) MQl-M refers to queries from sub- common attributes examined in studying social media in regards to reddits that have mentions of MHD but MHD is not the only focus, users with MHD are the vocabulary and syntax of posts, as well as (2) MQl-E identifies queries from subreddits that are explicitly for interactions with the platforms themselves (e.g., number of posts users and topics of MHD, and (3) MQl-S captures queries from and retweets). Findings from the research conducted thus far not subreddits focused on topics of self-harm and suicide. only highlight the vocabulary and syntax used by MHD individuals Method. To build sentiment/emotion profiles we follow the online but also the trends in interactions with other people on social framework in [11]; using MQl and TQl in lieu of SE query logs. media platforms and with the platforms themselves [19]. We create a vector capturing the sentiment/emotion of each snippet Research exploring the relationship between MHD and SE is in MQl and TQl by averaging the sentiment/emotion vectors for in its infancy. From a user perspective, Campbell et al. [4] discuss each word in the snippet. For sentiment, we use SentiWordNet [2], help seeking behavior (i.e., looking for resources to understand and which represents words as vectors of positive, negative, and objective. help with MHD) of users with MHD. Zhu et al. [24], on the other For emotion, we use EmoLexData [17], which represents words as hand, use query logs from a university web server to predict users vectors of afraid, amused, angry, annoyed, dont_care, happy, inspired, suffering from depression. Similarly, Zaman et al. [23] identify users and sad. Words that do not appear in the lexicons are set to 0 on all with self-esteem issues from user-provided Google search histories. vector elements, except objective and dont_care, which are set to 1. Xu et al. [20] instead evaluate the degree to which mood influences users’ interactions with SE. From a resource perspective, some 4 ANALYSIS researchers have looked at the emotion, sentiment, and opinion We explore TQl and MQl profiles from different perspectives. emerging from Web resources [8, 11], but only those retrieved for By Resources. We first average the sentiment/emotion vectors traditional users. Unfortunately, none of the aforementioned works representations of all resources in SERP from MQl and TQl (rows shine a light on the potential that SE’ responses (i.e., resources 1 -5 in Table 1). Objective is the predominant sentiment, whereas retrieved) have to alter the mental well-being of users with MHD. the emotions with the highest scores are dont_care and happy. We In our work, we take a first step towards addressing the gap we notice some significant differences between the profiles generated see in the literature by exploring what snippets feel when reacting to for TQl and MQl. The sentiment vector of MQl is less objective users with MHD, as to determine what feelings are being push onto than its counterpart from TQl, whereas MQl’s emotion vector this population that is already struggling with their own emotions. includes higher scores for angry, annoyed, inspired, and sad. The emotion vector of MQl has lower scores for amused and dont_care 3 DATA AND METHOD than TQl’s. When exploring the profiles of MQl’s categories, we In our exploration, we use the data and method below. observe that the emotion profile of MQl-M yields scores that are Data. Query logs from mainstream SE are seldom available for lower for afraid, slightly higher for happy, and remain the same for research and, to the best of our knowledge, non-existent from sad when compared to the emotion profile of TQl. searchers with MHD. Consequently, to enable sentiment/emotion By Queries. We also average sentiment/emotion profiles on a exploration we created two synthetic query logs: TQl and MQl, per query basis (rows 6-10 in Table 1). We observe a similar pattern which emulate pairs generated by traditional searchers as the one discussed for sentiment/emotion profiles generated at and individuals affected by MHD, respectively. result level. When digging into categories within MQl a few differ- For TQl, we use Yahoo Webscope’s search query tiny sample ences do emerge. The emotion profile of MQl-M has a lower value [21], which includes 4,458 queries (1,211 unigrams; the remaining for afraid than TQl, but now happy is aligned with the profile n-grams). Each query is associated with the corresponding SERP of TQl. The emotion profile of MQl-E has also changed, with (top-10 resources, as users do not often go past the first page when amused and sad scores now being attune with TQl’s. looking at resources [16]), retrieved using Google API. For MQl, we By Top-Ranked Result. As top-ranked resources in response use 4,418 synthetic queries (1,200 unigrams, the remaining n-grams to queries are the first users encounter on a SERP, we are interested to follow the query distribution of TQl) which we generate from in the sentiment/emotion that emerges from them. From profiles Reddit posts. For each query, we also include the corresponding reported in rows 11 - 15 of Table 1, we observe that the sentiment of SERP generated using Google API. (Note that for MQl and TQl top-ranked resources remains consistent with both previous analy- we keep only the resources that lead to snippets in English.) sis. However, the emotion profile of MQl has changed, as it now Averaged Sentiment Emotion Row Source by Pos Neg Obj Afraid Amused Angry Annoyed Dont_care Happy Inspired Sad Resources 1 TQl 5.547 3.330 91.123 3.560 6.561 4.913 4.584 43.853 25.360 6.555 4.614 2 MQl 6.032𝛽 4.170𝛽 89.798𝛽 3.511 6.317𝛽 5.294𝛽 4.889𝛽 40.572𝛽 25.478 9.113𝛽 4.828𝛽 3 MQl-M 6.131𝛽 4.030𝛽 89.839𝛽 3.307𝛽 6.275𝛽 5.358𝛽 4.918𝛽 40.713𝛽 25.609𝛼 9.155𝛽 4.666 4 MQl-E 6.007𝛽𝛾 4.279𝛽𝛿 89.714𝛽 3.634𝛿 6.404𝛼 5.239𝛽 4.852𝛽 40.411𝛽 25.391 9.233𝛽 4.835𝛽𝛾 5 MQl-S 5.962𝛽𝛿 4.200𝛽𝛿 89.839𝛽 3.589𝛿 6.273𝛽 5.284𝛽 4.895𝛽 40.589𝛽 25.437 8.958𝛽𝜂 4.974𝛽𝛿 Queries 6 TQl 5.543 3.330 91.127 3.561 6.552 4.897 4.577 43.921 25.325 6.557 4.609 7 MQl 6.057𝛽 4.227𝛽 89.716𝛽 3.535 6.319𝛽 5.345𝛽 4.898𝛽 40.604𝛽 25.367 9.069𝛽 4.863𝛽 8 MQl-M 6.130𝛽 4.028𝛽 89.842𝛽 3.307𝛽 6.275𝛼 5.360𝛽 4.918𝛽 40.708𝛽 25.612 9.153𝛽 4.667 9 MQl-E 6.002𝛽 4.277𝛽𝛿 89.721𝛽 3.634𝛿 6.400 5.247𝛽 4.852𝛽 40.403𝛽 25.402 9.231𝛽 4.831 10 MQl-S 5.961𝛽𝛾 4.198𝛽𝛾 89.840𝛽 3.588𝛿 6.273𝛽 5.286𝛽 4.896𝛽 40.589𝛽 25.439 8.957𝛽 4.972𝛽𝛾 Top-Ranked 11 TQl 5.679 3.251 91.070 3.463 6.382 4.908 4.524 43.867 25.387 6.557 4.911 12 MQl 5.993𝛽 4.254𝛽 89.753𝛽 3.285 6.342 5.545𝛽 5.108𝛽 40.081𝛽 26.033𝛼 8.640𝛽 4.966𝛽 13 MQl-M 6.237𝛽 4.035𝛽 89.727𝛽 3.074𝛼 6.466 5.588𝛽 5.143𝛽 40.116𝛽 26.178𝛼 8.551𝛽 4.884 14 MQl-E 5.845𝛾 4.413𝛽𝛿 89.742𝛽 3.243 6.218 5.507𝛽 5.039𝛽 40.346𝛽 25.922 8.822𝛽 4.903 15 MQl-S 5.901𝛾 4.311𝛽𝛾 89.788𝛽 3.527𝛾 6.343 5.540𝛽 5.140𝛽 39.795𝛽 26.000 8.552𝛽 5.104 Table 1: Sentiment/emotion profiles inferred from TQl and MQl (SERP generated using Google), where vector sum to 100. For statistical significance (two tail t test), 𝛼 p < 0.05 and 𝛽 p < 0.01 with respect to scores generated from TQl for the respective average type. Further, 𝛾 p < 0.05 and 𝛿 p < 0.01 indicate significance with respect to scores generated from MQl-M for the respective average type. Lastly, 𝜂 p < 0.05 and p < 0.01 for to scores generated from MQl-M and MQl-E, respectively. Averaged by Unigram N-gram Query Type Source Positive Angry Happy TQl 5.416 4.609 23.467 TQl MQl TQl MQl Unigram MQl 6.403𝛽 7.234𝛽 24.677𝛼 Resources 6.087 6.100 6.713 6.398𝛽 TQl 5.780 5.016 26.088 N-gram MQl 5.840 4.915 26.539 Queries 6.123 6.141 6.708 6.397𝛽 Table 3: Positive, angry, and happy scores for top-ranked re- Table 2: Amused scores for unigrams vs. n-grams. For statis- sults. Statistical significance (two tail t test), 𝛽 p < 0.01 w.r.t. tical significance (two tail t test), 𝛽 p < 0.01 w.r.t. TQl for the TQl for the analogous query length. respective average type and query length. has higher scores for angry, annoyed, happy, and inspired, as well respond abnormally to these categories. Most significant differences as a lower score in dont_care when compared to TQl’s emotion across categories occur when comparing MQl-M with respect to profile. MQl’s emotion vector at the top-ranked result has amused MQl-E and MQl-S. The sentiment profiles generated from MQl- and sad scores inline with TQl’s, yet the happy scores increased. E and MQl-S are less positive and more negative than MQl-M’s, When examining MQl-M, MQl-E, and MQl-S, we discovered a except when averaging by query, where only MQl-S is less positive, change from their sentiment profiles. The sentiment profiles of i.e., all categories are less positive overall. In terms of emotion MQl-E and MQl-S no longer have significant difference for posi- profiles, the only significant differences across the 3 categories are tive sentiment over TQl’s profile. The emotion profile of MQl-M for afraid, inspired, and sad. MQl-E and MQl-S emotion vectors has a lower score for afraid and a higher score for happy than have higher scores in afraid than MQl-M’s when averaging all TQl, but the difference for amused across the two profiles is no result and by query, but only MQl-S’s has higher scores for afraid longer significant. Similarly, the profiles of MQl-E and MQl-S when averaging by top-ranked resources. However, only averaging have amused and sad consistent with TQl. all result shows both MQl-E and MQl-S’s emotion profiles having Unigram vs N-grams. We explored the variations, if any, exhib- larger scores for sad than that of MQl-M. When averaging by ited in profiles emerging from unigram and n-gram queries. Among query, the emotion vector of MQl-S has a higher score for sad the most interesting results, captured in Table 2, we see that the than MQl-M’s. MQl-S’s inspired score is lower than both MQl- emotion vector of MQl has a lower amused score then TQl when M’s and MQl-E’s when averaging all resources. looking at n-grams, while with unigrams, MQl profile is aligned Safe Search. It is worth mentioning that we also examined sen- with TQl’s. The most notable differences between unigrams and n- timent/emotion profiles emerging when using Google SafeSearch grams with regard to top-ranked resources are summarized in Table to generate the corresponding SERP for each query in TQl and 3. MQl n-grams are alike in positive, angry, and happy with TQl n- MQl. We do so, based on our interest in examining how safe search grams, whereas MQl unigrams result in significantly higher values functionality handles inquiries related to MHD. Upon initial explo- in positive, angry, and happy when compared to TQl unigrams. ration of sentiment/emotion profiles we did not observe statistically MHD Categories. As MHD have varying levels of severity, we significant changes with respect to the profiles surfacing without look into the profiles emerging from MQl-M, MQl-E, and MQl-S, the use of safe search. For this reason, and due to space constrains, reported in rows 3-5, 8-10, and 13-15 of Table 1, to determine if SE we exclude detailed findings from this analysis. 5 DISCUSSION users by providing the number to the suicide lifeline in response Results from our analysis reveal significant changed between the to queries include words directly related to committing suicide. sentiment/emotion profiles of SE when responding to queries from While a step in the right direction, this only accounts for a small users suffering from MHD, when compared to traditional searchers. percentage of SE interactions initiated by users with MHD. Further, Most notably, the sentiment/emotion profiles originating from MQl we showed that the resources provided to these users in response express more sentiment and emotion overall. Our findings concur to their inquiries are more emotionally charged. Hence, there is a with the results reported by Kazai et al. [11] in regards to web demand to adjust SE retrieval and ranking algorithms to dampen resources having a high proportion of objective sentiment and the emotional weight MHD searchers are exposed to. Although dont-care, happy, and inspired emotions, yet, we witness more beyond the scope of this work, given the correlation between our changes in positive and negative sentiment, as well as angry and finding and those found in the social media domain, investigat- annoyed emotions. The increase towards more polar sentiments ing how users with MHD interact with SE interfaces and how the and negatively charged emotions is worrisome. It shows that users interfaces must adapt to enable support is crucial. with MHD are encountering resources that have the potential to negatively effect their mental health and may not provide them 6 LIMITATIONS AND FUTURE WORK with objective information to make decisions. We discuss limitations and future work emerging from our work. The evident changes in responses to users with MHD in regards SE. In this preliminary analysis, we examine resources retrieved to anger and annoyance are concerning, especially for top-ranked using Google (and its safe search functionality). Exploring a single resources. SE resources conveying such emotions to users with SE enabled us to set foundation in this area. Due to the varied MHD on a daily basis could have a negative effect on their mental retrieval and ranking strategies adopted by popular SE, we believe health [10]. The presence of anger and annoyance is also problem- it is necessary to extend analysis to alternative SE. atic when we look at the emotion profile of MQl-S, where we also Language. Currently, we only consider resources written in notice a higher score in sadness based on resources retrieved for English. Given the world-wide adoption of SE, extending our ex- queries from traditional users and users with less severe MHD. Re- ploration to other languages is a must. call that synthetic queries in MQl-S contain language from posts Analysis. We are aware that machine learning strategies are related to suicide and self-harm. While resources being higher in available for sentiment/emotion analysis. As our exploration fol- scores for angry, annoyed, and sad when responding to users with lows the framework presented by Kazai et al. [11], we use the same severe MHD compared to traditional users is anticipated, being lexicon strategy they adopt. We plan to explore machine learning exposed to these emotions may have a negative affect on users techniques, in addition to available lexicons explicitly for depres- mental health. There is also a surprising result when investigating sion (which have been built from social media resources [22]), in MQl-E, as when this category is averaged by query the value for future iterations of our work. amusement increases and the one for sadness decreases. This find- Data. Query logs are hard to obtain, especially for non-traditional ing prompted us to look into the queries that caused such changes. users, e.g., searchers affected by MHD. The lack of access to this We found that queries like "miserable", "disorder", "no fun", "new resource is what prompted us to create synthetic queries that would doctor", and "bed", lead to profiles with high amusement and low enable preliminary inspections. To do so, we assumed that users sadness scores. Out of context, some of these queries could be per- of subreddits we target are individuals who suffer from MHD, an ceived as jesting you’re "no fun", but in the context of MHD they assumption we cannot confirm. take on different connotations: having "no fun" or not being able to get out of "bed" are realities of users of suffering from MHD and are 7 CONCLUSION generally not looked at in an amused light. When exploring the sentiment/emotion profiles of MQl per We have presented the discoveries that have arisen from explor- category, we discovered that MQl-E’s and MQl-S’s profiles are ing the sentiment/emotion profiles of SE (Google in our case) for less positive and more negative, afraid, and sad than the profile inquiries common among users with MHD. Preliminary findings of MQl-M, showcasing that different levels of severity of MHD reveal significant differences across the sentiment/emotion profiles changes the sentiment/emotion profiles of content retrieved by SE. of SERP created by SE for searchers suffering from MHD vs. tra- While this pattern remains true for top-ranked resources on the ditional SE users. While these results are not surprising given the SERP, only MQl-S’s profiles show more of the afraid emotion. This context of our work, there was no prior documentation highlighting finding is interesting, given that we identify less significant changes the differences in emotional/sentiment profiles of SE results when overall when looking at just the top-ranked result in response to responding to users with MHD. With SE being a persuasive tech- all queries. The change in profile for top-ranked resources could be nology, presenting resources that evoke emotions in individuals due to Google pushing help hot-lines to the top-ranked resources experiencing MHD as opposed to remaining objectivity, could have when dealing with queries explicitly related to suicide and self harm, an effect on the mental health and decision making abilities of users however, more investigation is needed to confirm this theory. with MHD. Outcomes from this work reveal new research questions Findings emerging from our exploration reveals there is much to be addressed by the information retrieval and human-computer work to be done for SE to accommodate users with MHD. To start, interaction communities, including how query suggestions, rank- SE would require the ability to recognize this compromised pop- ing, and interface design can influence users with MHD. Further, ulation. Currently, SE like Google and Bing acknowledge these how existing SE should be adapted to recognize and better serve this user group. REFERENCES France) (SIGIR’19). Association for Computing Machinery, New York, NY, USA, [1] 99firms.com. 2019. Search Engine Statistics. Available at: https://99firms.com/ 1249–1252. https://doi.org/10.1145/3331184.3331353 blog/search-engine-statistics/. (accessed March 20, 2020). [21] Yahoo! 2019. Yahoo! Datasets. Available at: https://webscope.sandbox.yahoo. [2] Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. 2010. Sentiwordnet com/catalog.php?datatype=l. (accessed November 6, 2019). 3.0: an enhanced lexical resource for sentiment analysis and opinion mining.. In [22] Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Lrec, Vol. 10. 2200–2204. Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, and [3] Michael L Birnbaum, Sindhu Kiranmai Ernala, Asra F Rizvi, Munmun De Choud- Amit Sheth. 2017. Semi-Supervised Approach to Monitoring Clinical Depressive hury, and John M Kane. 2017. A collaborative approach to identifying social Symptoms in Social Media. In Proceedings of the 2017 IEEE/ACM International media markers of schizophrenia by employing machine learning and clinical Conference on Advances in Social Networks Analysis and Mining 2017 (Sydney, appraisals. Journal of medical Internet research 19, 8 (2017), e289. Australia) (ASONAM ’17). Association for Computing Machinery, New York, NY, [4] Andrew Campbell, Brad Ridout, Melina Linden, Brian Collyer, and John Dalgleish. USA, 1191–1198. https://doi.org/10.1145/3110025.3123028 2018. A preliminary understanding of search words used by children, teenagers [23] Anis Zaman, Rupam Acharyya, Henry Kautz, and Vincent Silenzio. 2019. Detect- and young adults in seeking information about depression and anxiety online. ing Low Self-Esteem in Youths from Web Search Data. In The World Wide Web Con- Journal of Technology in Human Services 36, 4 (2018), 208–221. ference (San Francisco, CA, USA) (WWW ’19). Association for Computing Machin- [5] J. Clement. 2019. U.S. internet usage penetration 2019, by age group. Available ery, New York, NY, USA, 2270–2280. https://doi.org/10.1145/3308558.3313557 at: https://www.statista.com/statistics/266587/percentage-of-internet-users-by- [24] Changye Zhu, Baobin Li, Ang Li, and Tingshao Zhu. 2016. Predicting depression age-groups-in-the-us/. (accessed March 20, 2020). from internet behaviors by time-frequency features. In 2016 IEEE/WIC/ACM [6] Munmun De Choudhury, Michael Gamon, Scott Counts, and Eric Horvitz. 2013. International Conference on Web Intelligence (WI). IEEE, 383–390. Predicting depression via social media. In Seventh international AAAI conference [25] Mark Zimmerman, Caroline Balling, Iwona Chelminski, and Kristy Dalrymple. on weblogs and social media. 2019. Symptom presence versus symptom intensity in understanding the severity [7] Munmun De Choudhury, Emre Kiciman, Mark Dredze, Glen Coppersmith, and of depression: Implications for documentation in electronic medical records. Mrinal Kumar. 2016. Discovering Shifts to Suicidal Ideation from Mental Health Journal of affective disorders 256 (2019), 344–347. Content in Social Media. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (San Jose, California, USA) (CHI ’16). Association for Computing Machinery, New York, NY, USA, 2098–2110. https://doi.org/10. 1145/2858036.2858207 [8] Gianluca Demartini and Stefan Siersdorfer. 2010. Dear Search Engine: What’s Your Opinion about...? Sentiment Analysis for Semantic Enrichment of Web Search Results. In Proceedings of the 3rd International Semantic Search Workshop (Raleigh, North Carolina, USA) (SEMSEARCH ’10). Association for Computing Machinery, New York, NY, USA, Article 4, 7 pages. https://doi.org/10.1145/ 1863879.1863883 [9] BJ Fogg. 2003. Persuasive technology: using computers to change what we think and do. Morgan Kaufmann. [10] Markham Heid. 2018. You Asked: Is It Bad for You to Read the News Constantly? Available at: https://time.com/5125894/is-reading-news-bad-for-you/. (accessed March 18, 2020). [11] Gabriella Kazai, Paul Thomas, and Nick Craswell. 2019. The Emotion Profile of Web Search. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1097–1100. [12] Zina Moukheiber. 2019. Mental Health Awareness Is On The Rise, But Access To Professionals Remains Dismal. Available at: forbes.com/sites/zinamoukheiber/2019/05/22/mental-health-awareness- is-on-the-rise-but-access-to-professionals-remains-dismal/#300040c37ba5. (accessed March 19, 2020). [13] National Institute of Mental Health. 2019. Mental Illness. Available at: https: //www.nimh.nih.gov/health/statistics/mental-illness.shtml. (accessed March 19, 2020). [14] Esteban A Ríssola, David E Losada, and Fabio Crestani. 2019. Discovering Latent Depression Patterns in Online Social Media. In Proceedings of the 10th Italian Information Retrieval Workshop (Padova, Italy) (IIR ’19). 13–16. http://ceur- ws.org/Vol-2441/paper10.pdf [15] H Andrew Schwartz, Johannes Eichstaedt, Margaret Kern, Gregory Park, Maarten Sap, David Stillwell, Michal Kosinski, and Lyle Ungar. 2014. Towards assessing changes in degree of depression through facebook. In Proceedings of the workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality. 118–125. [16] Dushyant Sharma, Rishabh Shukla, Anil Kumar Giri, and Sumit Kumar. 2019. A Brief Review on Search Engine Optimization. In 2019 9th International Conference on Cloud Computing, Data Science and Engineering (Confluence). IEEE, 687–692. [17] Kaisong Song, Wei Gao, Ling Chen, Shi Feng, Daling Wang, and Chengqi Zhang. 2016. Build emotion lexicon from the mood of crowd via topic-assisted joint non-negative matrix factorization. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. 773–776. [18] Sho Tsugawa, Yusuke Kikuchi, Fumio Kishino, Kosuke Nakajima, Yuichi Itoh, and Hiroyuki Ohsaki. 2015. Recognizing Depression from Twitter Activity. In Proceed- ings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). Association for Computing Machinery, New York, NY, USA, 3187–3196. https://doi.org/10.1145/2702123.2702280 [19] Nikhita Vedula and Srinivasan Parthasarathy. 2017. Emotional and Linguistic Cues of Depression from Social Media. In Proceedings of the 2017 International Conference on Digital Health (London, United Kingdom) (DH ’17). Association for Computing Machinery, New York, NY, USA, 127–136. https://doi.org/10.1145/ 3079452.3079465 [20] Luyan Xu, Xuan Zhou, and Ujwal Gadiraju. 2019. Revealing the Role of User Moods in Struggling Search Tasks. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris,