Instant messaging platforms such as Telegram became one of the main means of communication used by people all over the world. Most of them are home of several groups and channels that connect thousands of people focused on political topics. However, they have suffered with misinformation campaigns with a direct impact on electoral processes around the world. While some platforms, such as WhatsApp, took restrictive policies and measures to attenuate the issues arising from the abuse of their systems, others have emerged as alternatives, presenting little or no restrictions on content moderation or actions in combating misinformation. Telegram is one of those systems, which has been attracting more users and gaining popularity. In this work, we present the "Telegram Monitor", a web-based system that monitors the political debate in this environment and enables the analysis of the most shared content in multiple channels and public groups. Our system aims to allow journalists, researchers, and fact-checking agencies to identify trending conspiracy theories, misinformation campaigns, or simply to monitor the political debate in this space along the 2022 Brazilian elections. We hope our system can assist the combat of misinformation spreading through Telegram in Brazil.
Coordinated Information Dissemination on Telegram and Reddit During Political Turbulence: A Case Study of Venezuela in Global News Channels
Foundation for Research and Technology（研究与技术基金会）；Institute of Computer Science, Foundation for Research and Technology（研究与技术基金会计算机研究所）；Technical University of Crete（克里特理工大学）
The outbreak of COVID-19 had a huge global impact, and non-scientific beliefs and political polarization have significantly influenced the population's behavior. In this context, COVID vaccines were made available in an unprecedented time, but a high level of hesitance has been observed that can undermine community immunization. Traditionally, anti-vaccination attitudes are more related to conspiratorial thinking rather than political bias. In Brazil, a country with an exemplar tradition in large-scale vaccination programs, all COVID-related topics have also been discussed under a strong polit…
Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；State University of Southwestern of Bahia（巴伊亚西南州立大学）；Federal University of Bahia（巴伊亚联邦大学）
Since June 2013, when Brazil faced the largest and most significant mass protests in a generation, a political crisis is in course. In midst of this crisis, Brazilian politicians use social media to communicate with the electorate in order to retain or to grow their political capital. The problem is that many controversial topics are in course and deputies may prefer to avoid such themes in their messages. To characterize this behavior, we propose a method to accurately identify political and non-political tweets independently of the deputy who posted it and of the time it was posted. Moreover…
In this paper, we introduce a network-based methodology to study how clusters represented by political entities evolve over time. We constructed networks of voting data from the Brazilian Chamber of Deputies, where deputies are nodes and edges are represented by voting similarity among deputies. The Brazilian Chamber of deputies is characterized by a multi-party political system. Thus, we would expect a broad spectrum of ideas to be represented. Our results, however, revealed that plurality of ideas is not present at all: the effective number of communities representing ideas based on agreemen…
Online social networks such as Twitter are important platforms for spreading public opinion on a variety of subjects. The classification of users through the analysis of their posts on Twitter according to their opinion sharing can help marketing ads and political campaigns to focus on specific user groups. Community detection-based techniques are specially useful to classify Twitter users, as they do not require rule-based methods or labeled data to perform the clustering task. In this paper, we constructed networks using data related to political discussions in Brazil extracted from Twitter.…
Evolution of wartime discourse on Telegram: A comparative study of Ukrainian and Russian policymakers' communication before and after Russia's full-scale invasion of Ukraine
University of Bern（伯尔尼大学）；University of Vienna（维也纳大学）
In this work, we study the behavior of Brazilian politicians and political parties with the help of clustering algorithms for signed social networks. For this purpose, we extract and analyze a collection of signed networks representing voting sessions of the lower house of Brazilian National Congress. We process all available voting data for the period between 2011 and 2016, by considering voting similarities between members of the Congress to define weighted signed links. The solutions obtained by solving Correlation Clustering (CC) problems are the basis for investigating deputies voting net…
Messaging platforms, especially those with a mobile focus, have become increasingly ubiquitous in society. These mobile messaging platforms can have deceivingly large user bases, and in addition to being a way for people to stay in touch, are often used to organize social movements, as well as a place for extremists and other ne'er-do-well to congregate. In this paper, we present a dataset from one such mobile messaging platform: Telegram. Our dataset is made up of over 27.8K channels and 317M messages from 2.2M unique users. To the best of our knowledge, our dataset comprises the largest and …
Visual Political Communication in a Polarized Society: A Longitudinal Study of Brazilian Presidential Elections on Instagram
University of Amsterdam（阿姆斯特丹大学）；Federal University of Sao Paulo (Unifesp)（圣保罗联邦大学）；Johannes Gutenberg University Mainz (JGU)（美因茨约翰内斯古腾堡大学）；Dublin City University (DCU)（都柏林城市大学）
The Brazilian Vaccination Debate on YouTube: Topics, Perspectives, and Engagement Dynamics
Universidade Federal de Ouro Preto（欧罗普雷托联邦大学）；ISTI, National Research Council (CNR)（意大利国家研究委员会IST研究所）；University of Pisa（比萨大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）
Univ. Federal de Mato Grosso do Sul（南马托格罗索联邦大学）；Univ. Estadual do Sudoeste da Bahia（巴伊亚西南州立大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）；LIG（格勒诺布尔信息学实验室）；Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）
The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Facebook ads using word embeddi…
