Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg?

By Zi Chu, Steven Gianvecchio, Haining Wang, Sushil Jajodia. 2012

668 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Natural Language Processing, Signal Processing

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Citations in the corpus

Universal Language Model Fine-tuning for Text Classification (2018, background): “Text classification is a category of Natural Language Processing (NLP) tasks with real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al.”
Fine-tuned Language Models for Text Classification (2018, background): “…Processing (NLP) tasks with many important real-world applications such as spam, fraud, and bot detection (Jindal and Liu, 2007; Ngai et al., 2011; Chu et al., 2012), emergency response (Caragea et al., 2011), and commercial document classiﬁcation, such as for legal discovery (Roitblat et al.,…”
Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019, background): “In fact, some users mix manual postings with automated ones, resulting in accounts that blend human with bot behavior—dubbed cyborgs (Chu et al., 2012).”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “The easy switch of functionalities between human and bot offers cyborg users unique opportunities to spread fake news [15].”
The DARPA Twitter Bot Challenge (2016, result): “All teams benefited from previous influence-bot studies [1,3,9,16].”
A decade of social bot detection (2020, background): “ounts that are simultaneously driven by algorithms and by human intervention led to even more fine-grained definitions and cyborgs were introduced as either bot-assisted humans or human-assisted bots [3]. Instead, social scientists are typically more interested in the social or political implication”
Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo (2020, methodology): “Various natural language processing methods were used to classify social media content into several types [30], [31].”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, background): “Second, increasing evidence suggests the presence on social media of hybrid human-bot accounts (sometimes referred to as cyborgs) that perform automated actions with some human supervision (Chu et al. 2012; Clark et al. 2016).”
Deep Neural Networks for Bot Detection (2018, methodology): “…bot detection at the user level, all these methods still require the analysis of some historical user data, either by indirect data collection [10, 11, 13, 57], or, like in the case of BotOrNot [15], by interrogating the Twitter API (which imposes strict rate limits, making it impossible to do…”
A Survey on Computational Propaganda Detection (2020, background): “Such coordination is achieved by leveraging botnets (groups of fully automated accounts) [Zhang et al., 2016], cyborgs (partially automated) [Chu et al., 2012] and troll armies (human-driven) [Linvill and Patrick, 2018], known as sockpuppets [Kumar et al., 2017], Internet water army [Chen et al.,…”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “etecting and blocking fake news. Recent studies can be seen in, for example, in [Cheng et al. 2017;Shao et al. 2017], that analyze behavioral patterns of bots and trolls in fake news propagation, in [Chu et al. 2012], that automatically classifies humans, bots and cyborgs in terms of their profiles,”
How Does Social Media Impact Bitcoin Value? A Test of the Silent Majority Hypothesis (2018, background): “For one, adding external URLs to tweets is a common practice [11], and essential information then gets encapsulated at an external site; it cannot be decoded solely by analyzing (or reading) the tweets themselves.”