Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine

• URL: http://arxiv.org/abs/2307.12788v1
• Date: Mon, 24 Jul 2023 13:35:18 GMT
• Title: Analyzing the Strategy of Propaganda using Inverse Reinforcement Learning: Evidence from the 2022 Russian Invasion of Ukraine
• Authors: Dominique Geissler and Stefan Feuerriegel
• Abstract summary: The 2022 Russian invasion of Ukraine was accompanied by a large-scale, pro-Russian propaganda campaign on social media. Here, we analyze the strategy of the Twitter community using an inverse reinforcement learning approach. We show that bots respond predominantly to pro-invasion messages, while messages indicating opposition primarily elicit responses from humans.
• Score: 21.563820572163337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 2022 Russian invasion of Ukraine was accompanied by a large-scale, pro-Russian propaganda campaign on social media. However, the strategy behind the dissemination of propaganda has remained unclear, particularly how the online discourse was strategically shaped by the propagandists' community. Here, we analyze the strategy of the Twitter community using an inverse reinforcement learning (IRL) approach. Specifically, IRL allows us to model online behavior as a Markov decision process, where the goal is to infer the underlying reward structure that guides propagandists when interacting with users with a supporting or opposing stance toward the invasion. Thereby, we aim to understand empirically whether and how between-user interactions are strategically used to promote the proliferation of Russian propaganda. For this, we leverage a large-scale dataset with 349,455 posts with pro-Russian propaganda from 132,131 users. We show that bots and humans follow a different strategy: bots respond predominantly to pro-invasion messages, suggesting that they seek to drive virality; while messages indicating opposition primarily elicit responses from humans, suggesting that they tend to engage in critical discussions. To the best of our knowledge, this is the first study analyzing the strategy behind propaganda from the 2022 Russian invasion of Ukraine through the lens of IRL.

Related papers

• Characterizing and Detecting Propaganda-Spreading Accounts on Telegram [7.759087666892532]
  Information-based attacks on social media, such as disinformation campaigns and propaganda, are emerging cybersecurity threats. We propose a novel mechanism for detecting propaganda that capitalizes on the relationship between legitimate user messages and propaganda replies. Our method is faster, cheaper, and has a detection rate (97.6%) 11.6 percentage points higher than human moderators after seeing only one message from an account.
  arXiv  Detail & Related papers  (2024-06-12T11:07:27Z)
• Narratives of War: Ukrainian Memetic Warfare on Twitter [7.6603687980663775]
  The 2022 Russian invasion of Ukraine has seen an intensification in the use of social media by governmental actors in cyber warfare. We study this prominent example of memetic warfare through the lens of its narratives, and find them to be a key component of success. Our findings present a nuanced examination of Ukraine's influence operations and of the worldwide response to it, thus contributing new insights into the evolution of socio-technical systems in times of war.
  arXiv  Detail & Related papers  (2023-09-15T12:41:03Z)
• Opponents and proponents of the war in Ukraine in Russian social media: who are they? [0.0]
  I compare the political identities, values, and interests of social media users in Russia who hold a strong position for or against the war in Ukraine. I found that proponents of the war tend to have a weaker political identity. The values of the proponents more frequently align with those promoted by the Russian government.
  arXiv  Detail & Related papers  (2023-08-08T12:21:49Z)
• Russo-Ukrainian War: Prediction and explanation of Twitter suspension [47.61306219245444]
  This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of user accounts that may lead to this. We have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API. Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin fraud, spam, and advertisement campaigns.
  arXiv  Detail & Related papers  (2023-06-06T08:41:02Z)
• Multimodal Propaganda Processing [34.295018092278255]
  We introduce the task of multimodal propaganda processing, where the goal is to automatically analyze propaganda content. We believe that this task presents a long-term challenge to AI researchers and that successful processing of propaganda could bring machine understanding one important step closer to human understanding.
  arXiv  Detail & Related papers  (2023-02-17T05:49:55Z)
• Persuasion Strategies in Advertisements [68.70313043201882]
  We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies. We then formulate the task of persuasion strategy prediction with multi-modal learning. We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
  arXiv  Detail & Related papers  (2022-08-20T07:33:13Z)
• Twitter Dataset on the Russo-Ukrainian War [68.713984286035]
  We have initiated an ongoing dataset acquisition from Twitter API. The dataset has reached the amount of 57.3 million tweets, originating from 7.7 million users. We apply an initial volume and sentiment analysis, while the dataset can be used to further exploratory investigation towards topic analysis, hate speech, propaganda recognition, or even show potential malicious entities like botnets.
  arXiv  Detail & Related papers  (2022-04-07T12:33:06Z)
• The Spread of Propaganda by Coordinated Communities on Social Media [43.2770127582382]
  We analyze the spread of propaganda and its interplay with coordinated behavior on a large Twitter dataset about the 2019 UK general election. The combination of the use of propaganda and coordinated behavior allows us to uncover the authenticity and harmfulness of the different communities.
  arXiv  Detail & Related papers  (2021-09-27T13:39:10Z)
• Cross-Domain Learning for Classifying Propaganda in Online Contents [67.10699378370752]
  We present an approach to leverage cross-domain learning, based on labeled documents and sentences from news and tweets, as well as political speeches with a clear difference in their degrees of being propagandistic. Our experiments demonstrate the usefulness of this approach, and identify difficulties and limitations in various configurations of sources and targets for the transfer step.
  arXiv  Detail & Related papers  (2020-11-13T10:19:13Z)
• Russian trolls speaking Russian: Regional Twitter operations and MH17 [68.8204255655161]
  In 2018, Twitter released data on accounts identified as Russian trolls. We analyze the Russian-language operations of these trolls. We find that trolls' information campaign on the MH17 crash was the largest in terms of tweet count.
  arXiv  Detail & Related papers  (2020-05-13T19:48:12Z)

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.