Two Phase Based Spammer Detection in Weibo

Zheng, Xinhu; Wang, Jiamiao; Jie, Fei; Li, Lei

doi:10.1109/icdmw.2015.22

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Nilizadeh et al [29] use message propagation dynamics in neighborhoods defined by a collection of LDA-derived topics. Other researchers work with datasets derived from sites semantically similar to Twitter [52] (e.g. Weibo).…”

Section: Related Workmentioning

confidence: 99%

Emotional Bots: Content-based Spammer Detection on Social Media

Andriotis

Takasu

2018

2018 IEEE International Workshop on Information Forensics and Security (WIFS)

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Recent research indicates that a considerable amount of content on social media is generated by automated accounts. The automata present sophisticated behaviormimicking humans-aiming at evading traditional detection methods. In this paper, we present a supervised approach to detect automated accounts on Twitter using mainly contentbased features. We performed our experiments using four datasets that contain tweets from almost 20K malicious and benign accounts. Our methodology is lightweight and employs users' metadata, content and sentiment features. It performs well on unseen data (0.95 F1-score) reaching 95% precision and recall. This work also demonstrates that sentiment characteristics can add value to social spambot detection algorithms when combined with known features.

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Section: Related Workmentioning

confidence: 99%

Emotional Bots: Content-based Spammer Detection on Social Media

Andriotis

Takasu

2018

2018 IEEE International Workshop on Information Forensics and Security (WIFS)

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“…Users create status messages and give their opinions on wide range of topics [ 17 ]. Sina-Weibo also allows users to interact with others using tools such as mention (@, @username, which means that the message sender is willing to share this post with the user/users mentioned, retweet, which means the users are usually forwarding messages they are interested in from other users, hashtag (##, contents between## refer to a specific topic), and like, which is when users agree with messages posted by other users [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

Social Media Behavior and Emotional Evolution during Emergency Events

Guo

Zhuang

2021

Healthcare

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Online social networks have recently become a vital source for emergency event news and the consequent venting of emotions. However, knowledge on what drives user emotion and behavioral responses to emergency event developments are still limited. Therefore, unlike previous studies that have only explored trending themes and public sentiment in social media, this study sought to develop a holistic framework to assess the impact of emergency developments on emotions and behavior by exploring the evolution of trending themes and public sentiments in social media posts as a focal event developed. By examining the event timelines and the associated hashtags on the popular Chinese social media site Sina-Weibo, the 2019 Wuxi viaduct collapse accident was taken as the research object and the event timeline and the Sina-Weibo tagging function focused on to analyze the behaviors and emotional changes in the social media users and elucidate the correlations. It can conclude that: (i) There were some social media rules being adhered to and that new focused news from the same event impacted user behavior and the popularity of previous thematic discussions. (ii) While the most critical function for users appeared to express their emotions, the user foci changed when recent focus news emerged. (iii) As the news of the collapse deepened, the change in user sentiment was found to be positively correlated with the information released by personal-authentication accounts. This research provides a new perspective on the extraction of information from social media platforms in emergencies and social-emotional transmission rules.

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“…Spammers attempt to follow a lot of users and want to be followed back to increase their followers. [2][3][4] The study demonstrates that a quite part of users will follow strangers who follow them in Sina Weibo. 5 There are some differences between the following strategies of normal users and spammers.…”

Section: Introductionmentioning

confidence: 99%

“…[2][3][4] The study demonstrates that a quite part of users will follow strangers who follow them in Sina Weibo. 5 There are some differences between the following strategies of normal users and spammers.…”

Section: Introductionmentioning

confidence: 99%

GroupFound: An effective approach to detect suspicious accounts in online social networks

Feng

Pan

et al. 2017

International Journal of Distributed Sensor Networks

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Online social networks are an important part of people's life and also become the platform where spammers use suspicious accounts to spread malicious URLs. In order to detect suspicious accounts in online social networks, researchers make a lot of efforts. Most existing works mainly utilize machine learning based on features. However, once the spammers disguise the key features, the detection method will soon fail. Besides, such methods are unable to cope with the variable and unknown features. The works based on graph mainly use the location and social relationship of spammers, and they need to build a huge social graph, which leads to much computing cost. Thus, it is necessary to propose a lightweight algorithm which is hard to be evaded. In this article, we propose a lightweight algorithm GroupFound, which focuses on the structure of the local graph. As the bi-followers come from different social communities, we divide all accounts into different groups and compute the average number of accounts for these groups. We evaluate GroupFound on Sina Weibo dataset and find an appropriate threshold to identify suspicious accounts. Experimental results have demonstrated that our algorithm can accomplish a high detection rate of 86:27% at a low false positive rate of 8:54%.

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Two Phase Based Spammer Detection in Weibo

Cited by 7 publications

References 14 publications

Emotional Bots: Content-based Spammer Detection on Social Media

Emotional Bots: Content-based Spammer Detection on Social Media

Social Media Behavior and Emotional Evolution during Emergency Events

GroupFound: An effective approach to detect suspicious accounts in online social networks

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