@inproceedings{sakketou-etal-2022-temporal,
title = "Temporal Graph Analysis of Misinformation Spreaders in Social Media",
author = "Plepi, Joan and
Sakketou, Flora and
Geiss, Henri-Jacques and
Flek, Lucie",
editor = "Ustalov, Dmitry and
Gao, Yanjun and
Panchenko, Alexander and
Valentino, Marco and
Thayaparan, Mokanarangan and
Nguyen, Thien Huu and
Penn, Gerald and
Ramesh, Arti and
Jana, Abhik",
booktitle = "Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.textgraphs-1.10",
pages = "89--104",
abstract = "Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. Although the news domain is subject to rapid changes over time, the temporal dynamics of the spreaders{'} language and network have not been explored yet. In this paper, we analyze the users{'} time-evolving semantic similarities and social interactions and show that such patterns can, on their own, indicate misinformation spreading. Building on these observations, we propose a dynamic graph-based framework that leverages the dynamic nature of the users{'} network for detecting fake news spreaders. We validate our design choice through qualitative analysis and demonstrate the contributions of our model{'}s components through a series of exploratory and ablative experiments on two datasets.",
}
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<abstract>Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. Although the news domain is subject to rapid changes over time, the temporal dynamics of the spreaders’ language and network have not been explored yet. In this paper, we analyze the users’ time-evolving semantic similarities and social interactions and show that such patterns can, on their own, indicate misinformation spreading. Building on these observations, we propose a dynamic graph-based framework that leverages the dynamic nature of the users’ network for detecting fake news spreaders. We validate our design choice through qualitative analysis and demonstrate the contributions of our model’s components through a series of exploratory and ablative experiments on two datasets.</abstract>
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%0 Conference Proceedings
%T Temporal Graph Analysis of Misinformation Spreaders in Social Media
%A Plepi, Joan
%A Sakketou, Flora
%A Geiss, Henri-Jacques
%A Flek, Lucie
%Y Ustalov, Dmitry
%Y Gao, Yanjun
%Y Panchenko, Alexander
%Y Valentino, Marco
%Y Thayaparan, Mokanarangan
%Y Nguyen, Thien Huu
%Y Penn, Gerald
%Y Ramesh, Arti
%Y Jana, Abhik
%S Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F sakketou-etal-2022-temporal
%X Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. Although the news domain is subject to rapid changes over time, the temporal dynamics of the spreaders’ language and network have not been explored yet. In this paper, we analyze the users’ time-evolving semantic similarities and social interactions and show that such patterns can, on their own, indicate misinformation spreading. Building on these observations, we propose a dynamic graph-based framework that leverages the dynamic nature of the users’ network for detecting fake news spreaders. We validate our design choice through qualitative analysis and demonstrate the contributions of our model’s components through a series of exploratory and ablative experiments on two datasets.
%U https://aclanthology.org/2022.textgraphs-1.10
%P 89-104
Markdown (Informal)
[Temporal Graph Analysis of Misinformation Spreaders in Social Media](https://aclanthology.org/2022.textgraphs-1.10) (Plepi et al., TextGraphs 2022)
ACL