Bot detection in twitter landscape using unsupervised learning
Author:
Affiliation:
1. Lahore University of Management Sciences, Pakistan
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3396956.3401801
Reference7 articles.
1. David A Broniatowski Amelia M Jamison SiHua Qi Lulwah AlKulaib Tao Chen Adrian Benton Sandra C Quinn and Mark Dredze. 2018. Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. American journal of public health 108 10 (2018) 1378–1384. David A Broniatowski Amelia M Jamison SiHua Qi Lulwah AlKulaib Tao Chen Adrian Benton Sandra C Quinn and Mark Dredze. 2018. Weaponized health communication: Twitter bots and Russian trolls amplify the vaccine debate. American journal of public health 108 10 (2018) 1378–1384.
2. Zhouhan Chen and Devika Subramanian. 2018. An unsupervised approach to detect spam campaigns that use botnets on Twitter. arXiv preprint arXiv:1804.05232(2018). Zhouhan Chen and Devika Subramanian. 2018. An unsupervised approach to detect spam campaigns that use botnets on Twitter. arXiv preprint arXiv:1804.05232(2018).
3. Hunting Malicious Bots on Twitter: An Unsupervised Approach
4. The US 2016 presidential election & Russia’s troll farms
5. Symantec Security ResponseSecurity Response Team Symantec Security Response AuthorSymantec Security ResponseSecurity Response TeamSymantec’s Security Response Symantec and Symantec. [n.d.]. How to Spot a Twitter Bot. Symantec Security ResponseSecurity Response Team Symantec Security Response AuthorSymantec Security ResponseSecurity Response TeamSymantec’s Security Response Symantec and Symantec. [n.d.]. How to Spot a Twitter Bot.
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