@article{
 title = {Forex trading and Twitter: Spam, bots, and reputation manipulation},
 type = {article},
 identifiers = {[object Object]},
 id = {60663a83-de39-34dd-ab7d-4f9eab5451c4},
 created = {2018-04-13T12:49:05.429Z},
 file_attached = {true},
 profile_id = {ce0c01be-b53e-353f-835d-b11ba1ee5eb8},
 group_id = {bd0f1172-1660-3fe3-b7d0-9f2d0b4051ad},
 last_modified = {2018-04-13T13:49:15.434Z},
 tags = {DOLFINS_T3.1,DOLFINS_WP3,DOLFINS_working_paper},
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 starred = {false},
 authored = {false},
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 abstract = {Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification model is constructed. The model then classifies all the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD). The Twitter stance is compared to the actual currency rates by applying the event study methodology, well-known in financial economics. It turns out that there are large differences in Twitter stance distribution and potential trading returns between the four groups of Twitter users: trading robots, spammers, trading companies, and individual traders. Additionally, we observe attempts of reputation manipulation by post festum removal of tweets with poor predictions, and deleting/reposting of identical tweets to increase the visibility without tainting one's Twitter timeline.},
 bibtype = {article},
 author = {Mozetič, Igor and Gabrovšek, Peter and Novak, Petra Kralj}
}

{"_id":"BncTA3CbXBDvvh65Z","bibbaseid":"mozeti-gabrovek-novak-forextradingandtwitterspambotsandreputationmanipulation","downloads":0,"creationDate":"2018-04-13T17:11:47.871Z","title":"Forex trading and Twitter: Spam, bots, and reputation manipulation","author_short":["Mozetič, I.","Gabrovšek, P.","Novak, P., K."],"year":null,"bibtype":"article","biburl":null,"bibdata":{"title":"Forex trading and Twitter: Spam, bots, and reputation manipulation","type":"article","identifiers":"[object Object]","id":"60663a83-de39-34dd-ab7d-4f9eab5451c4","created":"2018-04-13T12:49:05.429Z","file_attached":"true","profile_id":"ce0c01be-b53e-353f-835d-b11ba1ee5eb8","group_id":"bd0f1172-1660-3fe3-b7d0-9f2d0b4051ad","last_modified":"2018-04-13T13:49:15.434Z","tags":"DOLFINS_T3.1,DOLFINS_WP3,DOLFINS_working_paper","read":false,"starred":false,"authored":false,"confirmed":false,"hidden":false,"private_publication":false,"abstract":"Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification model is constructed. The model then classifies all the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD). The Twitter stance is compared to the actual currency rates by applying the event study methodology, well-known in financial economics. It turns out that there are large differences in Twitter stance distribution and potential trading returns between the four groups of Twitter users: trading robots, spammers, trading companies, and individual traders. Additionally, we observe attempts of reputation manipulation by post festum removal of tweets with poor predictions, and deleting/reposting of identical tweets to increase the visibility without tainting one's Twitter timeline.","bibtype":"article","author":"Mozetič, Igor and Gabrovšek, Peter and Novak, Petra Kralj","bibtex":"@article{\n title = {Forex trading and Twitter: Spam, bots, and reputation manipulation},\n type = {article},\n identifiers = {[object Object]},\n id = {60663a83-de39-34dd-ab7d-4f9eab5451c4},\n created = {2018-04-13T12:49:05.429Z},\n file_attached = {true},\n profile_id = {ce0c01be-b53e-353f-835d-b11ba1ee5eb8},\n group_id = {bd0f1172-1660-3fe3-b7d0-9f2d0b4051ad},\n last_modified = {2018-04-13T13:49:15.434Z},\n tags = {DOLFINS_T3.1,DOLFINS_WP3,DOLFINS_working_paper},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {false},\n hidden = {false},\n private_publication = {false},\n abstract = {Currency trading (Forex) is the largest world market in terms of volume. We analyze trading and tweeting about the EUR-USD currency pair over a period of three years. First, a large number of tweets were manually labeled, and a Twitter stance classification model is constructed. The model then classifies all the tweets by the trading stance signal: buy, hold, or sell (EUR vs. USD). The Twitter stance is compared to the actual currency rates by applying the event study methodology, well-known in financial economics. It turns out that there are large differences in Twitter stance distribution and potential trading returns between the four groups of Twitter users: trading robots, spammers, trading companies, and individual traders. Additionally, we observe attempts of reputation manipulation by post festum removal of tweets with poor predictions, and deleting/reposting of identical tweets to increase the visibility without tainting one's Twitter timeline.},\n bibtype = {article},\n author = {Mozetič, Igor and Gabrovšek, Peter and Novak, Petra Kralj}\n}","author_short":["Mozetič, I.","Gabrovšek, P.","Novak, P., K."],"urls":{"Paper":"https://bibbase.org/service/mendeley/e7ad1df1-57da-3801-b924-2e51ef162a9d/file/4022d503-174a-7f49-2484-5388f5eef06f/Forex_trading_and_Twitter_Spam_bots_and_reputation_manipulation.pdf.pdf"},"bibbaseid":"mozeti-gabrovek-novak-forextradingandtwitterspambotsandreputationmanipulation","role":"author","downloads":0},"search_terms":["forex","trading","twitter","spam","bots","reputation","manipulation","mozetič","gabrovšek","novak"],"keywords":[],"authorIDs":[]}