Modeling Information Narrative Detection and Evolution on Telegram during the Russia-Ukraine War
- URL: http://arxiv.org/abs/2409.07684v1
- Date: Thu, 12 Sep 2024 01:18:57 GMT
- Title: Modeling Information Narrative Detection and Evolution on Telegram during the Russia-Ukraine War
- Authors: Patrick Gerard, Svitlana Volkova, Louis Penafiel, Kristina Lerman, Tim Weninger,
- Abstract summary: In February 2022, the Russian Federation's full-scale invasion of Ukraine took place.
A multitude of information narratives emerged within both pro-Russian and pro-Ukrainian communities online.
This study aims to model narrative evolution and uncover the underlying mechanisms driving them.
- Score: 6.732843525962766
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Following the Russian Federation's full-scale invasion of Ukraine in February 2022, a multitude of information narratives emerged within both pro-Russian and pro-Ukrainian communities online. As the conflict progresses, so too do the information narratives, constantly adapting and influencing local and global community perceptions and attitudes. This dynamic nature of the evolving information environment (IE) underscores a critical need to fully discern how narratives evolve and affect online communities. Existing research, however, often fails to capture information narrative evolution, overlooking both the fluid nature of narratives and the internal mechanisms that drive their evolution. Recognizing this, we introduce a novel approach designed to both model narrative evolution and uncover the underlying mechanisms driving them. In this work we perform a comparative discourse analysis across communities on Telegram covering the initial three months following the invasion. First, we uncover substantial disparities in narratives and perceptions between pro-Russian and pro-Ukrainian communities. Then, we probe deeper into prevalent narratives of each group, identifying key themes and examining the underlying mechanisms fueling their evolution. Finally, we explore influences and factors that may shape the development and spread of narratives.
Related papers
- A Longitudinal Study of Italian and French Reddit Conversations Around
the Russian Invasion of Ukraine [1.002138130221506]
This study delves into the conversations within the largest Italian and French Reddit communities, specifically examining how the Russian invasion of Ukraine affected online interactions.
We use a dataset with over 3 million posts (i.e., comments and submissions) to describe the patterns of moderation activity.
We found changes in moderators' behavior, who became more active during the first month of the war.
arXiv Detail & Related papers (2024-02-07T16:15:52Z) - Narratives of Collective Action in YouTube's Discourse on Veganism [0.0]
We use natural language processing to operationalize a theoretical framework of moral narratives specific to the vegan movement.
Our analysis reveals that several narrative types, as defined by the theory, are empirically present in the data.
Video narratives advocating social fight, whether through protest or through efforts to convert others to the cause, are associated with a stronger sense of collective action in the respective comments.
arXiv Detail & Related papers (2024-01-17T13:44:36Z) - InfoPattern: Unveiling Information Propagation Patterns in Social Media [59.67008841974645]
InfoPattern centers on the interplay between language and human ideology.
The demo is capable of: (1) red teaming to simulate adversary responses from opposite ideology communities; (2) stance detection to identify the underlying political sentiments in each message; (3) information propagation graph discovery to reveal the evolution of claims across various communities over time.
arXiv Detail & Related papers (2023-11-27T09:12:35Z) - News and Misinformation Consumption in Europe: A Longitudinal
Cross-Country Perspective [49.1574468325115]
This study investigated information consumption in four European countries.
It analyzed three years of Twitter activity from news outlet accounts in France, Germany, Italy, and the UK.
Results indicate that reliable sources dominate the information landscape, although unreliable content is still present across all countries.
arXiv Detail & Related papers (2023-11-09T16:22:10Z) - Discovering collective narratives shifts in online discussions [3.6231158294409482]
We propose a systematic narrative discovery framework that fills the gap by combining change point detection, semantic role labeling (SRL), and automatic aggregation of narrative fragments into narrative networks.
We evaluate our model with synthetic and empirical data two-Twitter corpora about COVID-19 and 2017 French Election.
Results demonstrate that our approach can recover major narrative shifts that correspond to the major events.
arXiv Detail & Related papers (2023-07-17T15:00:04Z) - Automated multilingual detection of Pro-Kremlin propaganda in newspapers
and Telegram posts [5.886782001771578]
The full-scale conflict between the Russian Federation and Ukraine generated an unprecedented amount of news articles and social media data.
This study analyses how the media affected and mirrored public opinion during the first month of the war using news articles and Telegram news channels in Ukrainian, Russian, Romanian and English.
We propose and compare two methods of multilingual automated pro-Kremlin propaganda identification, based on Transformers and linguistic features.
arXiv Detail & Related papers (2023-01-25T14:25:37Z) - Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases.
We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions.
Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
arXiv Detail & Related papers (2023-01-14T18:58:42Z) - Adherence to Misinformation on Social Media Through Socio-Cognitive and
Group-Based Processes [79.79659145328856]
We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation.
We make the case that polarization and misinformation adherence are closely tied.
arXiv Detail & Related papers (2022-06-30T12:34:24Z) - Fake news agenda in the era of COVID-19: Identifying trends through
fact-checking content [0.8594140167290099]
We introduce a novel Markov-inspired computational method for identifying topics in tweets.
We collected data from Twitter accounts of two Brazilian fact-checking outlets.
Our method resulted in an important technique to cluster topics in a wide range of scenarios.
arXiv Detail & Related papers (2020-12-20T19:35:25Z) - Information Consumption and Social Response in a Segregated Environment:
the Case of Gab [74.5095691235917]
This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic.
We find that there are no strong statistical differences in the social response to questionable and reliable content.
Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
arXiv Detail & Related papers (2020-06-03T11:34:25Z) - Mining Disinformation and Fake News: Concepts, Methods, and Recent
Advancements [55.33496599723126]
disinformation including fake news has become a global phenomenon due to its explosive growth.
Despite the recent progress in detecting disinformation and fake news, it is still non-trivial due to its complexity, diversity, multi-modality, and costs of fact-checking or annotation.
arXiv Detail & Related papers (2020-01-02T21:01:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.