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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>Since social media such as Facebook and X (formerly known as Twitter) have permeated various aspects of daily life, people have strong incentives to influence information dissemination on these platforms and differentiate their content from the fierce competition. Existing dissemination strategies typically employ marketing techniques, such as seeking publicity through renowned actors or targeted advertising placements. Despite their various forms, most simply spread information to strengthen user impressions without conducting formal analyses of specific influence enhancement. And coupled with high costs, most fall short of expectations. To this end, we ingeniously formulate the task of social media dissemination as poisoning attacks, which influence specified content\u2019s dissemination among target users by intervening in some users\u2019 social media behaviors (including retweeting, following, and profile modifying). Correspondingly, we propose a novel poisoning attack, Influence-based Social Media Attack (ISMA) to generate discrete poisoning behaviors, which is difficult to achieve with existing attacks. In ISMA, we first contribute an efficient influence evaluator to quantify the spread influence of poisoning behaviors. Based on the estimated influence, we then present an imperceptible hierarchical selector and a profile modification method ProMix to select influential behaviors to poison. Notably, our attack is driven by custom attack objectives, which allows one to flexibly design different optimization goals to change the information flow, which could solve the blindness of existing influence maximization methods. Besides, behaviors such as retweeting are gentle and simple to implement. These properties make our attack more cost-effective and practical. Extensive experiments on two large-scale real-world datasets demonstrate the superiority of our method as it significantly outperforms baselines, and additionally, the proposed evaluator\u2019s analysis of user influence provides new insights for influence maximization on social media.<\/jats:p>","DOI":"10.1145\/3654673","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T11:54:11Z","timestamp":1711540451000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Attacking Social Media via Behavior Poisoning"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4937-0590","authenticated-orcid":false,"given":"Chenwang","family":"Wu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China School of Data Science, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3507-9607","authenticated-orcid":false,"given":"Defu","family":"Lian","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9630-795X","authenticated-orcid":false,"given":"Yong","family":"Ge","sequence":"additional","affiliation":[{"name":"The University of Arizona Eller College of Management, Tucson, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4088-1266","authenticated-orcid":false,"given":"Min","family":"Zhou","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Naman Agarwal Brian Bullins and Elad Hazan. 2016. 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