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Web"],"published-print":{"date-parts":[[2017,5,31]]},"abstract":"<jats:p>It is often difficult to separate the highly capable \u201cexperts\u201d from the average worker in crowdsourced systems. This is especially true for challenge application domains that require extensive domain knowledge. The problem of stock analysis is one such domain, where even the highly paid, well-educated domain experts are prone to make mistakes. As an extremely challenging problem space, the \u201cwisdom of the crowds\u201d property that many crowdsourced applications rely on may not hold.<\/jats:p>\n          <jats:p>\n            In this article, we study the problem of evaluating and identifying experts in the context of SeekingAlpha and StockTwits, two crowdsourced investment services that have recently begun to encroach on a space dominated for decades by large investment banks. We seek to understand the quality and impact of content on collaborative investment platforms, by empirically analyzing complete datasets of SeekingAlpha articles (9 years) and StockTwits messages (4 years). We develop sentiment analysis tools and correlate contributed content to the historical performance of relevant stocks. While SeekingAlpha articles and StockTwits messages provide minimal correlation to stock performance in aggregate, a subset of experts contribute more valuable (predictive) content. We show that these authors can be easily identified by user interactions, and investments based on their analysis significantly outperform broader markets. This effectively shows that even in challenging application domains, there is a\n            <jats:italic>secondary or indirect<\/jats:italic>\n            wisdom of the crowds.\n          <\/jats:p>\n          <jats:p>Finally, we conduct a user survey that sheds light on users\u2019 views of SeekingAlpha content and stock manipulation. We also devote efforts to identify potential manipulation of stocks by detecting authors controlling multiple identities.<\/jats:p>","DOI":"10.1145\/3027487","type":"journal-article","created":{"date-parts":[[2017,5,8]],"date-time":"2017-05-08T12:26:59Z","timestamp":1494246419000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Value and Misinformation in Collaborative Investing Platforms"],"prefix":"10.1145","volume":"11","author":[{"given":"Tianyi","family":"Wang","sequence":"first","affiliation":[{"name":"Tsinghua University and University of California, Santa Barbara, CA"}]},{"given":"Gang","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara and Virginia Tech"}]},{"given":"Bolun","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, CA"}]},{"given":"Divya","family":"Sambasivan","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, CA"}]},{"given":"Zengbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, CA"}]},{"given":"Xing","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua University"}]},{"given":"Haitao","family":"Zheng","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, CA"}]},{"given":"Ben Y.","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara, CA"}]}],"member":"320","published-online":{"date-parts":[[2017,5,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1344411.1344413"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367587"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2012.34"},{"key":"e_1_2_1_4_1","volume-title":"Proc. of ICWSM.","author":"Akoglu Leman","year":"2013","unstructured":"Leman Akoglu , Rishi Chandy , and Christos Faloutsos . 2013 . 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Robust sentiment detection on Twitter from biased and noisy data. In Proc. of COLING."},{"key":"e_1_2_1_7_1","volume-title":"Proc. of CEAS.","author":"Benevenuto Fabr\u00edcio","year":"2010","unstructured":"Fabr\u00edcio Benevenuto , Gabriel Magno , Tiago Rodrigues , and Virgilio Almeida . 2010 . Detecting spammers on Twitter . In Proc. of CEAS. Fabr\u00edcio Benevenuto, Gabriel Magno, Tiago Rodrigues, and Virgilio Almeida. 2010. Detecting spammers on Twitter. In Proc. of CEAS."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-03689-2_3"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2010.12.007"},{"key":"e_1_2_1_10_1","volume-title":"Proc. of SAIS.","author":"Brown Eric D.","year":"2012","unstructured":"Eric D. Brown . 2012 . Will Twitter make you a better investor? A look at sentiment, user reputation and their effect on the stock market . In Proc. of SAIS. Eric D. Brown. 2012. Will Twitter make you a better investor? 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