Drift in Online Social Media

Mullick, Ankan; Bhandari, Anindya; Niranjan, Abhishek; Sckhar, Nitesh; Garg, Shrey; Bubna, Riya; Roy, Mayank

doi:10.1109/iemcon.2018.8614746

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Chakraborty et al [22,23] utilized these different popularity signals for online news recommendations. Mullick et al [24] experimented topic drift event and characteristics in online comments.…”

Section: Related Workmentioning

confidence: 99%

Public Sphere 2.0: Targeted Commenting in Online News Media

Mullick

Ghosh

Dutt

et al. 2019

Lecture Notes in Computer Science

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With the increase in online news consumption, to maximize advertisement revenue, news media websites try to attract and retain their readers on their sites. One of the most effective tools for reader engagement is commenting, where news readers post their views as comments against the news articles. Traditionally, it has been assumed that the comments are mostly made against the full article. In this work, we show that present commenting landscape is far from this assumption. Because the readers lack the time to go over an entire article, most of the comments are relevant to only particular sections of an article. In this paper, we build a system which can automatically classify comments against relevant sections of an article. To implement that, we develop a deep neural network based mechanism to find comments relevant to any section and a paragraph wise commenting interface to showcase them. We believe that such a data driven commenting system can help news websites to further increase reader engagement.

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Section: Related Workmentioning

confidence: 99%

Public Sphere 2.0: Targeted Commenting in Online News Media

Mullick

Ghosh

Dutt

et al. 2019

Lecture Notes in Computer Science

Self Cite

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“…Users often discuss various aspects of the original rumor, including event details and evidential support. As rumors proliferate across complex social networks, dialogues may shift from the initial event to specific topics, 8,9 occasionally leading to topic drift 10,11 . Relying solely on a singular keyword as the target fails to capture the nuanced focus of user stances, as illustrated by the solitary target‐based scenario in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Neural network approaches for rumor stance detection: Simulating complex rumor propagation systems

Li,

Yang,

Wang

et al. 2024

Concurrency and Computation

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SummaryThis research introduces a comprehensive suite of neural network models designed to tackle the challenging task of rumor stance detection within the framework of simulating complex rumor propagation systems. Our objective centers on accurately modeling the intricate structures of rumor dialogues and propagation patterns to identify user stances—whether they are in support, denial, questioning, or commenting on rumors. Unlike conventional methods that rely on simplistic keyword targeting and fail in the nuanced context of social networks, our models delve into the complexities of dialogue and propagation structures, offering a more precise and insightful analysis of rumor dynamics. In addressing the simulation and modeling of complex systems, our approach specifically focuses on the elaborate interaction networks that underpin rumor spread and reception. While our methodology does not directly engage with brain‐like computing paradigms, it reflects a similar level of sophistication in handling layered and complex information flows, analogous to cognitive processes in understanding and interpreting human communications. Employing a hierarchical attention mechanism, our models adeptly parse through multitiered dialogue sequences, effectively distinguishing between various indicators of user stances. This allows for a nuanced and detailed representation of the rumor ecosystem, significantly enhancing the accuracy of stance detection. Through rigorous testing on diverse datasets, our approach has demonstrated superior performance over existing models, thereby establishing a new benchmark in the field.

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