Network Analysis of Three Twitter Functions: Favorite, Follow and Mention

Kato, Shoko; Koide, Akihiro; Fushimi, Takayasu; Saito, Kazumi; Motoda, Hiroshi

doi:10.1007/978-3-642-32541-0_26

Cited by 13 publications

(5 citation statements)

References 7 publications

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“…We extract the 1.5 egocentric network E 1.5 (v), where v are the accounts in C 1 and C 2 . Although multiple studies observed that the degree for the mention network follows heavy-tailed distributions (e.g., [15]), in order to understand the topological structure, we further investigate the concrete goodness of fit (see Appendix A). The scale-free nature of the mention network (i.e., degree distribution that follows a power law) implies a very high heterogeneity level in user behavior, which is expected for human activity phenomena [4,20].…”

Section: Mention Graphmentioning

confidence: 99%

Twitter

Almaatouq

Alabdulkareem

Nouh

et al. 2014

Proceedings of the 2014 ACM Conference on Web Science

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Spam in Online Social Networks (OSNs) is a systemic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors, as well as negatively affecting users' engagement. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics (i.e., profile properties and social interactions). Our analysis includes over 100 million tweets collected over the course of one month, generated by approximately 30 million distinct user accounts, of which over 7% are suspended or removed due to abusive behaviors and other violations. We show that there exist two behaviorally distinct categories of twitter spammers and that they employ different spamming strategies. The users in these two categories demonstrate different individual properties as well as social interaction patterns. As the Twitter spammers continuously keep creating newer accounts upon being caught, a behavioral understanding of their spamming behavior will be vital in the design of future social media defense mechanisms.

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Section: Mention Graphmentioning

confidence: 99%

Twitter

Almaatouq

Alabdulkareem

Nouh

et al. 2014

Proceedings of the 2014 ACM Conference on Web Science

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“…One study analyzed user behavior with regard to favorite features, comparing Twitter with Fickler [6]. Another study applied the method of network analysis to the Favorite, Follow, and Mention functions on Twitter [7]. A large-scale survey sought to understand the motivation of using the Favorite function on Twitter [8].…”

Section: Related Workmentioning

confidence: 99%

The metrics of keywords to understand the difference between Retweet and Like in each category

Sekimoto,

Seki,

Yoshida

et al. 2020

Preprint

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The purpose of this study is to clarify what kind of news is easily retweeted and what kind of news is easily Liked. We believe these actions, retweeting and Liking, have different meanings for users. Understanding this difference is important for understanding people's interest in Twitter. To analyze the difference between retweets (RT) and Likes on Twitter in detail, we focus on word appearances in news titles. First, we calculate basic statistics and confirm that tweets containing news URLs have different RT and Like tendencies compared to other tweets. Next, we compared RTs and Likes for each category and confirmed that the tendency of categories is different. Therefore, we propose metrics for clarifying the differences in each action for each category used in the χ-square test in order to perform an analysis focusing on the topic. The proposed metrics are more useful than simple counts and TF-IDF for extracting meaningful words to understand the difference between RTs and Likes. We analyzed each category using the proposed metrics and quantitatively confirmed that the difference in the role of retweeting and Liking appeared in the content depending on the category. Moreover, by aggregating tweets chronologically, the results showed the trend of RT and Like as a list of words and clarified how the characteristic words of each week were related to current events for retweeting and Liking.

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“…Figure 7 shows the degree distribution of the mention network. Although multiple studies observed that the degree for the mention network follows heavy-tailed distributions (e.g., [27], in order to understand the topological structure, we further investigate the concrete goodness of fit [2]. The scale-free nature of the mention network (i.e., degree distribution that follows a power law) implies a very high heterogeneity level in user behavior, which is expected for human activity phenomena [9,32].…”

Section: Mention Graphmentioning

confidence: 99%

If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts

Almaatouq

Shmueli

Nouh

et al. 2016

Int. J. Inf. Secur.

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Spam in Online Social Networks (OSNs) is a systemic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors, as well as negatively affecting users' engagement. As spammers continuously keep creating newer accounts and evasive techniques upon being caught, a deeper understanding of their spamming strategies is vital to the design of future social media defense mechanisms. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics. Our analysis includes over 100 million messages collected from Twitter over the course of one month. We show that there exist two behaviorally distinct categories of spammers and that they employ different spamming strategies. Then, we illustrate how users in these two categories demonstrate different individual properties as well as social interaction patterns. Finally, we analyze the detectability of spam accounts with respect to three categories of features, namely, content attributes, social interactions, and profile properties.

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Network Analysis of Three Twitter Functions: Favorite, Follow and Mention

Cited by 13 publications

References 7 publications

Twitter

Twitter

The metrics of keywords to understand the difference between Retweet and Like in each category

If it looks like a spammer and behaves like a spammer, it must be a spammer: analysis and detection of microblogging spam accounts

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