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

Chu, Zi; Gianvecchio, Steven; Wang, Haining; Jajodia, Sushil

doi:10.1109/tdsc.2012.75

Cited by 538 publications

(384 citation statements)

References 32 publications

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“…Zombie fans certainly interfere in analyzing the social influence. A small number of empirical researches have been conducted on recognizing zombie fans [41][42][43]. The existing studies were mostly subject to the Twitter platform.…”

Section: Data Preprocessingmentioning

confidence: 99%

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Liu

Lin

2017

Complexity

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Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.

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Section: Data Preprocessingmentioning

confidence: 99%

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Liu

Lin

2017

Complexity

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“…The authors explain how spammers try to acquire large numbers of follower links in the social network in order to modify the ranking of their tweets by search engines. Chu, Gianvecchio, Wang, and Jajodia (2012) propose a classification system based on a set of measurements with a collection of over 500 K accounts with the main goal of observing the difference among human, bot, and cyborg in terms of tweeting behavior, tweet content, and account properties.…”

Section: Previous Workmentioning

confidence: 99%

Detecting malicious tweets in trending topics using a statistical analysis of language

Martínez-Romo

Araujo

2013

Expert Systems with Applications

164

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a b s t r a c tTwitter spam detection is a recent area of research in which most previous works had focused on the identification of malicious user accounts and honeypot-based approaches. However, in this paper we present a methodology based on two new aspects: the detection of spam tweets in isolation and without previous information of the user; and the application of a statistical analysis of language to detect spam in trending topics. Trending topics capture the emerging Internet trends and topics of discussion that are in everybody's lips. This growing microblogging phenomenon therefore allows spammers to disseminate malicious tweets quickly and massively. In this paper we present the first work that tries to detect spam tweets in real time using language as the primary tool. We first collected and labeled a large dataset with 34 K trending topics and 20 million tweets. Then, we have proposed a reduced set of features hardly manipulated by spammers. In addition, we have developed a machine learning system with some orthogonal features that can be combined with other sets of features with the aim of analyzing emergent characteristics of spam in social networks. We have also conducted an extensive evaluation process that has allowed us to show how our system is able to obtain an F-measure at the same level as the best state-ofthe-art systems based on the detection of spam accounts. Thus, our system can be applied to Twitter spam detection in trending topics in real time due mainly to the analysis of tweets instead of user accounts.

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“…Discussion provided in [48] Bot-generated messages [47] Spam or misleading information sent to EM Social media account Random Forest Classifier for bot detection [49] Table 2. Sensing security threats and proposed EM countermeasures.…”

Section: Social Media Management Applicationsmentioning

confidence: 99%

“…A first large-scale and systematic attempt to detect and defend against twitter spam has been recently presented in [49], where a Random Forest classifier is used to tell whether a message has been sent from a human, a bot, a human-assisted bot or a bot-assisted human. The latter refers to users employing applications that automatically post periodic updates in their absence.…”

Section: Social Mediamentioning

confidence: 99%

A Review of Cyber Threats and Defence Approaches in Emergency Management

2013

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Emergency planners, first responders and relief workers increasingly rely on computational and communication systems that support all aspects of emergency management, from mitigation and preparedness to response and recovery. Failure of these systems, whether accidental or because of malicious action, can have severe implications for emergency management. Accidental failures have been extensively documented in the past and significant effort has been put into the development and introduction of more resilient technologies. At the same time researchers have been raising concerns about the potential of cyber attacks to cause physical disasters or to maximise the impact of one by intentionally impeding the work of the emergency services. Here, we provide a review of current research on the cyber threats to communication, sensing, information management and vehicular technologies used in emergency management. We emphasise on open issues for research, which are the cyber threats that have the potential to affect emergency management severely and for which solutions have not yet been proposed in the literature.

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

Cited by 538 publications

References 32 publications

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

Detecting malicious tweets in trending topics using a statistical analysis of language

A Review of Cyber Threats and Defence Approaches in Emergency Management

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