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Twitter is increasingly used for political, advertising and marketing campaigns, where the main aim is to influence users to support specific causes, individuals or groups. We propose a novel methodology for mining and analyzing Twitter campaigns, which includes: (i) collecting tweets and detecting topics relating to a campaign; (ii) mining important campaign topics using scientometrics measures; (iii) modelling user interests using hashtags and topical entropy; (iv) identifying influential users using an adapted PageRank score; and (v) various metrics and visualization techniques for identifying bot-like activities. While this methodology is generalizable to multiple campaign types, we demonstrate its effectiveness on the 2017 German federal election.
Twitter has become a vital social media platform while an ample amount of malicious Twitter bots exist and induce undesirable social effects. Successful Twitter bot detection proposals are generally supervised, which rely heavily on large-scale datas
Twitter users operated by automated programs, also known as bots, have increased their appearance recently and induced undesirable social effects. While extensive research efforts have been devoted to the task of Twitter bot detection, previous metho
Twitter bot detection has become an important and challenging task to combat misinformation and protect the integrity of the online discourse. State-of-the-art approaches generally leverage the topological structure of the Twittersphere, while they n
Twitter has become a major social media platform since its launching in 2006, while complaints about bot accounts have increased recently. Although extensive research efforts have been made, the state-of-the-art bot detection methods fall short of ge
Over the past couple of years, anecdotal evidence has emerged linking coordinated campaigns by state-sponsored actors with efforts to manipulate public opinion on the Web, often around major political events, through dedicated accounts, or trolls. Al