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Web"],"published-print":{"date-parts":[[2023,5,31]]},"abstract":"<jats:p>Social media platforms such as Twitter or StockTwits are widely used for sharing stock market opinions between investors, traders, and entrepreneurs. Empirically, previous work has shown that the content posted on these social media platforms can be leveraged to predict various aspects of stock market performance. Nonetheless, actors on these social media platforms may not always have altruistic motivations and may instead seek to influence stock trading behavior through the (potentially misleading) information they post. While a lot of previous work has sought to analyze how social media can be used to predict the stock market, there remain many questions regarding the quality of the predictions and the behavior of active users on these platforms. To this end, this article seeks to address a number of open research questions: Which social media platform is more predictive of stock performance? What posted content is actually predictive, and over what time horizon? How does stock market posting behavior vary among different users? Are all users trustworthy or do some user\u2019s predictions consistently mislead about the true stock movement? To answer these questions, we analyzed data from Twitter and StockTwits covering almost 5 years of posted messages spanning 2015 to 2019. The results of this large-scale study provide a number of important insights among which we present the following: (i)\u00a0StockTwits is a more predictive source of information than Twitter, leading us to focus our analysis on StockTwits; (ii) on StockTwits, users\u2019 self-labeled sentiments are correlated with the stock market but are only slightly predictive in aggregate over the short-term; (iii) there are at least three clear types of temporal predictive behavior for users over a 144 days horizon: short, medium, and long term; and (iv) consistently incorrect users who are reliably wrong tend to exhibit what we conjecture to be \u201cbotlike\u201d post content and their removal from the data tends to improve stock market predictions from self-labeled content.<\/jats:p>","DOI":"10.1145\/3532856","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T11:47:05Z","timestamp":1664452025000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["A User-Centric Analysis of Social Media for Stock Market Prediction"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1807-430X","authenticated-orcid":false,"given":"Mohamed Reda","family":"Bouadjenek","sequence":"first","affiliation":[{"name":"Deakin University, Geelong, Victoria, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7984-8394","authenticated-orcid":false,"given":"Scott","family":"Sanner","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, Ontario, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0370-0622","authenticated-orcid":false,"given":"Ga","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Toronto, Toronto, Ontario, Canada"}]}],"member":"320","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102597"},{"key":"e_1_3_2_3_2","first-page":"78","volume-title":"Proceedings of the International AAAI Conference on Web and Social Media a (ICWSM\u201911)","author":"Kumar Shamanth","year":"2011","unstructured":"Shamanth Kumar, Geoffrey Barbier, Mohammad Ali Abbasi, and Huan Liu. 2011. 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