• Corpus ID: 52830602

Bot-hunter: A Tiered Approach to Detecting & Characterizing Automated Activity on Twitter

@inproceedings{Beskow2018BothunterAT,
  title={Bot-hunter: A Tiered Approach to Detecting \& Characterizing Automated Activity on Twitter},
  author={David M. Beskow and Kathleen M. Carley},
  year={2018}
}
As malicious automated agents, or bots, are increasingly used to manipulate the global marketplace of information and beliefs, their detection, characterization, and at times neutralization is an important aspect of a national security operations. Unhindered, these information campaigns, assisted by automated agents, can begin slowly changing a society and its norms. Within this context, we seek to lay the groundwork for bot-hunter, a Tiered Approach to bot detection and characterization, while… 

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References

SHOWING 1-10 OF 24 REFERENCES

Using Random String Classification to Filter and Annotate Automated Accounts

This research proposes using random string detection applied to user names to filter twitter streams for potential bot accounts and thereby generating annotated data.

BotOrNot: A System to Evaluate Social Bots

BotOrNot, a publicly-available service that leverages more than one thousand features to evaluate the extent to which a Twitter account exhibits similarity to the known characteristics of social bots, is presented.

Online Human-Bot Interactions: Detection, Estimation, and Characterization

This work presents a framework to detect social bots on Twitter, and describes several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.

Detecting Spam in a Twitter Network

This article examines spam around a one-time Twitter meme—“robotpickuplines” and shows the existence of structural network differences between spam accounts and legitimate users, highlighting challenges in disambiguating spammers from legitimate users.

Who is tweeting on Twitter: human, bot, or cyborg?

This paper proposes a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg on Twitter and demonstrates the efficacy of the proposed classification system.

Suspended accounts in retrospect: an analysis of twitter spam

This study examines the abuse of online social networks at the hands of spammers through the lens of the tools, techniques, and support infrastructure they rely upon and identifies an emerging marketplace of illegitimate programs operated by spammers.

Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum

It is found that political bots have a small but strategic role in the referendum conversations: the family of hashtags associated with the argument for leaving the EU dominates, different perspectives on the issue utilize different levels of automation, and less than 1 percent of sampled accounts generate almost a third of all the messages.

#bias: Measuring the Tweeting Behavior of Propagandists

This work studies the tweeting behavior of Twitter propagandists, users who consistently expreses the same opinion or ideology, focusing on two online communities: the 2010 Nevada senate race and the 2011 debt-ceiling debate.

Five Incidents, One Theme: Twitter Spam as a Weapon to Drown Voices of Protest

This work inspects five political events from 2011 and 2012 that revolved around popular Twitter hashtags which were inundated with spam tweets intended to overwhelm the original content and finds that the nature of spam varies sufficiently across incidents such that generalizations are hard to draw.

Malicious accounts: Dark of the social networks