Towards Understanding Malicious Actions on Twitter

In this study we investigate the characteristics of malicious account behaviors on Twitter based on the analysis of the published data archive. We investigate emergent behavior of malicious accounts that Twitter tagged as connected to state-backed information operations, identified as malicious and removed from the Twitter network. We focus on the analysis of four types of malicious accounts’ features: (1) Account reputation, (2) Account tweeting frequency, (3) Age of account and (4) Account activity score. With the use of descriptive statistics and unsupervised learning, we attempt to extend past research that defined behavioral patterns of malicious actors on Twitter. Our research contributes to the understanding of behavior of malicious actors and enriches current research in that area. In this paper we analyze the dataset published by Twitter in January 2019, which contains details on suspended malicious accounts’ activities initiated in Bangladesh.

[1]  D. Taussky,et al.  Twitter , 2020, American journal of clinical oncology.

[2]  Pasi Luukka,et al.  A novel similarity classifier with multiple ideal vectors based on k-means clustering , 2018, Decis. Support Syst..

[3]  Kristina Lerman,et al.  Analyzing the Digital Traces of Political Manipulation: The 2016 Russian Interference Twitter Campaign , 2018, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[4]  Abraham Seidmann,et al.  When Social Media Delivers Customer Service: Differential Customer Treatment in the Airline Industry , 2018, MIS Q..

[5]  Hani Safadi,et al.  Social Media Affordances for Connective Action: An Examination of Microblogging Use During the Gulf of Mexico Oil Spill , 2017, MIS Q..

[6]  Omer F. Rana,et al.  Can We Predict a Riot? Disruptive Event Detection Using Twitter , 2017, ACM Trans. Internet Techn..

[7]  Filippo Menczer,et al.  Online Human-Bot Interactions: Detection, Estimation, and Characterization , 2017, ICWSM.

[8]  Emilio Ferrara,et al.  Social Bots Distort the 2016 US Presidential Election Online Discussion , 2016, First Monday.

[9]  Filippo Menczer,et al.  BotOrNot: A System to Evaluate Social Bots , 2016, WWW.

[10]  Huan Liu,et al.  The fragility of Twitter social networks against suspended users , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[11]  Emilio Ferrara,et al.  "Manipulation and abuse on social media" by Emilio Ferrara with Ching-man Au Yeung as coordinator , 2015, SIGWEB Newsl..

[12]  David W. McDonald,et al.  Dissecting a Social Botnet: Growth, Content and Influence in Twitter , 2015, CSCW.

[13]  Michel van de Velden,et al.  Online profiling and clustering of Facebook users , 2015, Decis. Support Syst..

[14]  Di Jiang,et al.  Integrating Social and Auxiliary Semantics for Multifaceted Topic Modeling in Twitter , 2014, TOIT.

[15]  Emiliano De Cristofaro,et al.  Paying for Likes?: Understanding Facebook Like Fraud Using Honeypots , 2014, Internet Measurement Conference.

[16]  V. S. Subrahmanian,et al.  Using sentiment to detect bots on Twitter: Are humans more opinionated than bots? , 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

[17]  Fabrício Benevenuto,et al.  Reverse engineering socialbot infiltration strategies in Twitter , 2014, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[18]  Saini Jacob Soman,et al.  Detecting malicious tweets in trending topics using clustering and classification , 2014, 2014 International Conference on Recent Trends in Information Technology.

[19]  Sushil Jajodia,et al.  Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? , 2012, IEEE Transactions on Dependable and Secure Computing.

[20]  Robert Faris,et al.  Mapping Russian Twitter , 2012 .

[21]  Jacob Ratkiewicz,et al.  Detecting and Tracking Political Abuse in Social Media , 2011, ICWSM.

[22]  Hila Becker,et al.  Beyond Trending Topics: Real-World Event Identification on Twitter , 2011, ICWSM.

[23]  Kyumin Lee,et al.  Seven Months with the Devils: A Long-Term Study of Content Polluters on Twitter , 2011, ICWSM.

[24]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[25]  Ricardo Mendes.  Troops, Trolls and Troublemakers: A Global Inventory of Organized Social Media Manipulation , 2017 .

[26]  Form 8-K,et al.  UNITED STATES SECURITIES AND EXCHANGE COMMISSION , 2017 .

[27]  Abdolreza Abhari,et al.  Cluster-discovery of Twitter messages for event detection and trending , 2015, J. Comput. Sci..

[28]  Benjamin Waugh,et al.  Twitter Deception and Influence: Issues of Identity, Slacktivism, and Puppetry , 2014 .

[29]  Barry Wellman,et al.  Geography of Twitter networks , 2012, Soc. Networks.

[30]  Robert Gorwa.  Computational Propaganda in Poland: False Amplifiers and the Digital Public Sphere , 2022 .