Bandwagon Effect in Facebook Discussion Groups

Wang, Keith C.; Lai, Chun-Ming; Wang, Teng; Wu, S. Felix

doi:10.1145/2818869.2818914

Cited by 9 publications

(2 citation statements)

References 12 publications

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“…In the bandwagon effect model proposed by Wang et al [6], the numbers of comments with respect to each time window after a post has been created can be used to build matrices for each public page G to predict the final number of comments by machine learning and statistical methods. In other words, two post threads post A and post B are likely to have the same scale of cascades if in each timestamp i such that:…”

Section: Prediction Model and Evaluation Methodsmentioning

confidence: 99%

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Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem

2020

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Online social network (OSN) users are increasingly interacting with each other via articles, comments, and responses. When access control mechanisms are weak or absent, OSNs are perceived by attackers as rich environments for influencing public opinions via fake news posts or influencing commercial transactions via practices such as phishing. This has led to a body of research looking at potential ways to predict OSN user behavior using social science concepts such as conformity and the bandwagon effect. In this paper, we address the question of how social recommendation systems affect the occurrence of malicious URLs on Facebook, based on the assumption that there are no differences among recommendation systems in terms of delivering either legitimate or harmful information to users. Next, we use temporal features to build a prediction framework with >75% accuracy to predict increases in certain user group behaviors. Our effort involves the demarcation of URL classes, from malicious URLs viewed as causing significant damage to annoying spam messages and advertisements. We offer this analysis to better understand OSN user sensors reactions to various categories of malicious URLs in order to mitigate their effects.

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Section: Prediction Model and Evaluation Methodsmentioning

confidence: 99%

“…We are interested in why malicious URLs often occur on only some post threads. We applied the model proposed by Wang et al [6] to gain insight into how those malicious URLs may influence the growth of the conversation.…”

Section: Post-level Influencementioning

confidence: 99%

Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem

2020

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How Did the Discussion Go: Discourse Act Classification in Social Media Conversations

Dutta

Chakraborty

Das

2018

Unsupervised and Semi-Supervised Learning

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Jump on the Bandwagon? – Characterizing Bandwagon Phenomenon in Online NBA Fan Communities

Wang

Zhang

Han

et al. 2020

Lecture Notes in Computer Science

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Online user dynamics has been actively studied in recent years and bandwagon behavior is one of the most representative topics which can provide valuable insights for user identity change. Many previous studies have characterized bandwagon users and leveraged such characteristics to tackle practical problems such as community loyalty prediction. However, very few of them have investigated bandwagon dynamics from a longterm perspective. In this work, we focus on characterizing and predicting long-term bandwagon user behaviors in the context of online fan loyalty. Using a dataset collected from NBA-related discussion forums on Reddit, we trace the long-term loyalty status of bandwagon fans to capture their latent behavioral characteristics and then propose a computational model to predict their next sport season loyalty status with their home teams. Our analyses reveal that bandwagoning for most fans is a temporary switch and most of them will be back in the long term. In addition, online fans with different loyalty levels to their home teams have demonstrated different behaviors in various aspects, such as activity level, language usage and reply network properties. We then propose a model based on such behavioral characteristics to predict their next-season loyalty status. Its promising performance demonstrates the effectiveness of our behavior characterization.

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Bandwagon Effect in Facebook Discussion Groups

Cited by 9 publications

References 12 publications

Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem

Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem

How Did the Discussion Go: Discourse Act Classification in Social Media Conversations

Jump on the Bandwagon? – Characterizing Bandwagon Phenomenon in Online NBA Fan Communities

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