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

By Zi Chu, Steven Gianvecchio, Haining Wang, Sushil Jajodia. Asia-Pacific Computer Systems Architecture Conference, 2010

613 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/123f62c9393ebc6ecd4afad7fef474d44db9ac0f

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Citations in the corpus

Text Classification Algorithms: A Survey (2019, background): “, email and text message spam filtering) [36].”
Online Human-Bot Interactions: Detection, Estimation, and Characterization (2017, background): “Chu et al. built a classiﬁcation system identifying accounts controlled by humans, bots, and cyborgs (Chu et al. 2010; Chu et al. 2012).”
The anatomy of online deception: what makes automated text convincing? (2016, background): “Social media networks are being increasingly overwhelmed by software robots (known as bots) which automatically generate content to inform, influence, and deceive genuine users [5].”
Bots, #StrongerIn, and #Brexit: Computational Propaganda during the UK-EU Referendum (2016, background): “Analyzing sentiment on social media such as Twitter is difficult.[11], [12] Obviously, contributions using none of these hashtags are not captured in this data set.”
The Twitter of Babel: Mapping World Languages through Microblogging Platforms (2012, background): “If not suitably addressed, in fact, their presence could induce wrong conclusions on the day-to-day behavior of the average person [38].”
Deep Neural Networks for Bot Detection (2018, methodology): “niques have been designed to require smaller samples of user activity, and fewer labeled examples of bot and human users. Examples of such trend include the classification system proposed by Chu etal.[10, 11], the system based on crowd-sourcing designed by Wang et al. [57], the detection techniques ”
Detecting and Tracking Political Abuse in Social Media (2011, neutral): “Several other promising features could be used as input to a classifier, such as the age of the accounts involved in spreading a meme, the reputation of users based on other memes they have contributed, and other features from bot detection methods (Chu et al. 2010).”
Tampering with Twitter’s Sample API (2018, background): “[62] try to classify users as bots, humans, or cyborgs based upon the user’s behavioral patterns.”
Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb (2013, background): “’ Although we handpicked just one example of a bot to show the impact of even a single nonhuman Twitter user, an algorithmic approach could possibly yield a more exhaustive classi ﬁ cation of who is human, bot or cyborg (cf. Chu et al. 2010; Grier et al. 2010).”
Finding and Characterizing Information Warfare Campaigns (2020, background): “A few research efforts have attempted to solve this problem by leveraging a multi-class machine learning approach, placing accounts into multiple classification ‘buckets‘ instead of just two [65, 134].”
Evaluation in artificial intelligence: from task-oriented to ability-oriented measurement (2017, methodology): “Similarly, the detection of bots in social networks (sybils) and crowdsourcing platforms rely on tests that are variants of CAPTCHAs, the Turing Test, or the observation and analysis of user profiles and behaviour (Chu et al, 2010; Wang et al, 2012).”
Automated Media: An Institutional Theory Perspective on Algorithmic Media Production and Consumption (2014, background): “They are also playing an increasingly prominent role in areas of online content creation such as tweets, where a large number of tweets are automatically generated by algorithmically driven bots (Chu et al., 2011).”
Automatic deception detection: Methods for finding fake news (2015, neutral): “Outside of the analysis of content comes the use of metadata and telltale behavior of questionable sources (Chu, Gianvecchio, Wang & Jajodia, 2010).”