Disinformation campaigns on social media, involving coordinated activities from malicious accounts towards manipulating public opinion, have become increasingly prevalent. There has been growing evidence of social media abuse towards influencing politics and social issues in other countries, raising numerous concerns. The identification and prevention of coordinated campaigns has become critical to tackling disinformation at its source. Existing approaches to detect malicious campaigns make strict assumptions about coordinated behaviours, such as malicious accounts perform synchronized actions or share features assumed to be indicative of coordination. Others require part of the malicious accounts in the campaign to be revealed in order to detect the rest. Such assumptions significantly limit the effectiveness of existing approaches. In contrast , we propose AMDN (Attentive Mixture Density Network ) to automatically uncover coordinated group behaviours from account activities and interactions between accounts, based on temporal point processes. Furthermore, we leverage the learned model to understand and explain the behaviours of coordinated accounts in disinformation campaigns. We find that the average influence between coordinated accountsis the highest, whereas these accounts are not much influenced by regular accounts. We evaluate the effectiveness of the proposed method on Twitter data related to Russian interference in US Elections.Additionally, we identify disinformation campaigns in COVID-19 data collected from Twitter, and provide the first evidence and analysis of existence of coordinated disinformation campaigns in the ongoing pandemic .
Disinformation campaigns on social media, involving coordinated activities from malicious accounts towards manipulating public opinion, have become increasingly prevalent. Existing approaches to detect coordinated accounts either make very strict assumptions about coordinated behaviours, or require part of the malicious accounts in the coordinated group to be revealed in order to detect the rest. To address these drawbacks , we propose a generative model, AMDN-HAGE (Attentive Mixture Density Network with Hidden Account Group Estimation) which jointly models account activities and hidden group behaviours based on Temporal Point Processes (TPP) and Gaussian Mixture Model (GMM), to capture inherent characteristics of coordination which is, accounts that coordinate must strongly influence each other's activities, and collectively appear anomalous from normal accounts. To address the challenges of optimizing the proposed model, we provide a bilevel optimization algorithm with theoretical guarantee on convergence. We verified the effectiveness of the proposed method and training algorithm on real-world social network data collected from Twitter related to coordinated campaigns from Russia's Internet Research Agency targeting the 2016 U.S. Presidential Elections, and to identify coordinated campaigns related to the COVID-19 pandemic. Leveraging the learned model, we find that the average influence between coordinated account pairs is the highest.On COVID-19, we found coordinated group spreading anti-vaccination, anti-masks conspiracies that suggest the pandemic is a hoax and political scam .
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There has been growing evidence of social media abuse towards influencing politics and social issues in other countries, raising numerous concerns. The identification and prevention of coordinated campaigns has become critical to tackling disinformation at its source.
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malicious campaigns make
coordinated accounts either make very
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such as malicious accounts perform synchronized actions or share features assumed to be indicative of coordination. Others
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campaign
coordinated group
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Such assumptions significantly limit the effectiveness of existing approaches. In contrast
To address these drawbacks
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AMDN
a generative model, AMDN-HAGE
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) to automatically uncover coordinated group behaviours from
with Hidden Account Group Estimation) which jointly models
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interactions between accounts, based on temporal point processes. Furthermore, we leverage the learned model to understand and explain the behaviours of coordinated accounts in disinformation campaigns. We find that the average influence between coordinated accountsis the highest, whereas these accounts are not much influenced by regular accounts. We evaluate
hidden group behaviours based on Temporal Point Processes (TPP) and Gaussian Mixture Model (GMM), to capture inherent characteristics of coordination which is, accounts that coordinate must strongly influence each other's activities, and collectively appear anomalous from normal accounts. To address the challenges of optimizing the proposed model, we provide a bilevel optimization algorithm with theoretical guarantee on convergence. We verified
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on Twitter data related to Russian interference in US Elections.Additionally, we identify disinformation campaigns in
and training algorithm on real-world social network data collected from Twitter related to coordinated campaigns from Russia's Internet Research Agency targeting the 2016 U.S. Presidential Elections, and to identify coordinated campaigns related to the
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data collected from Twitter, and provide the first evidence and analysis of existence of coordinated disinformation campaigns in the ongoing pandemic
pandemic. Leveraging the learned model, we find that the average influence between coordinated account pairs is the highest.On COVID-19, we found coordinated group spreading anti-vaccination, anti-masks conspiracies that suggest the pandemic is a hoax and political scam