The Spread of Propaganda by Coordinated Communities on Social Media
- URL: http://arxiv.org/abs/2109.13046v1
- Date: Mon, 27 Sep 2021 13:39:10 GMT
- Title: The Spread of Propaganda by Coordinated Communities on Social Media
- Authors: Kristina Hristakieva, Stefano Cresci, Giovanni Da San Martino, Mauro
Conti, Preslav Nakov
- Abstract summary: 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.
- Score: 43.2770127582382
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large-scale manipulations on social media have two important characteristics:
(i) use of \textit{propaganda} to influence others, and (ii) adoption of
coordinated behavior to spread it and to amplify its impact. Despite the
connection between them, these two characteristics have so far been considered
in isolation. Here we aim to bridge this gap. In particular, we analyze the
spread of propaganda and its interplay with coordinated behavior on a large
Twitter dataset about the 2019 UK general election. We first propose and
evaluate several metrics for measuring the use of propaganda on Twitter. Then,
we investigate the use of propaganda by different coordinated communities that
participated in the online debate. The combination of the use of propaganda and
coordinated behavior allows us to uncover the authenticity and harmfulness of
the different communities. Finally, we compare our measures of propaganda and
coordination with automation (i.e., bot) scores and Twitter suspensions,
revealing interesting trends. From a theoretical viewpoint, we introduce a
methodology for analyzing several important dimensions of online behavior that
are seldom conjointly considered. From a practical viewpoint, we provide new
insights into authentic and inauthentic online activities during the 2019 UK
general election.
Related papers
- 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) - Trust and Believe -- Should We? Evaluating the Trustworthiness of
Twitter Users [5.695742189917657]
Fake news on social media is a major problem with far-reaching negative repercussions on both individuals and society.
In this work, we create a model through which we hope to offer a solution that will instill trust in social network communities.
Our model analyses the behaviour of 50,000 politicians on Twitter and assigns an influence score for each evaluated user.
arXiv Detail & Related papers (2022-10-27T06:57:19Z) - Detecting Propaganda Techniques in Memes [32.209606526323945]
We propose a new multi-label multimodal task: detecting the type of propaganda techniques used in memes.
We create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.
Our analysis of the corpus shows that understanding both modalities together is essential for detecting these techniques.
arXiv Detail & Related papers (2021-08-07T11:56:52Z) - A Study on Herd Behavior Using Sentiment Analysis in Online Social
Network [1.5673338088641469]
This paper represents and analyze the capacity of diverse strategies to predict critical opinions from online social networking sites.
Social media becomes a good outlet since the last decades to share the opinions globally.
This study demonstrates the evaluation of sentiment analysis techniques using social media contents.
arXiv Detail & Related papers (2021-07-25T05:22:35Z) - SOK: Seeing and Believing: Evaluating the Trustworthiness of Twitter
Users [4.609388510200741]
Currently, there is no automated way of determining which news or users are credible and which are not.
In this work, we created a model which analysed the behaviour of50,000 politicians on Twitter.
We classified the political Twitter users as either trusted or untrusted using random forest, multilayer perceptron, and support vector machine.
arXiv Detail & Related papers (2021-07-16T17:39:32Z) - News consumption and social media regulations policy [70.31753171707005]
We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation.
Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content.
The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
arXiv Detail & Related papers (2021-06-07T19:26:32Z) - 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) - Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
We argue that it is critical to understand what user attributes potentially cause users to share fake news.
In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities.
We propose a principled approach to alleviating selection bias in fake news dissemination.
arXiv Detail & Related papers (2020-10-20T19:37:04Z) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z)
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.