Research
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Auditing Political Exposure Bias: Algorithmic Amplification on Twitter/X Approaching the 2024 U.S. Presidential Election
By Jinyi Ye
[from the abstract] Approximately 50% of tweets in X’s user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: what political content are users exposed to beyond their established networks, and how might this influence democratic discourse online? Due to the black-box nature and constant evolution of social media algorithms, much remains unknown about this aspect of users’ content exposure
By Federico Cinus
[from the abstract] Coordinated information operations remain a persistent challenge on social media, despite platform efforts to curb them. While previous research has primarily focused on identifying these operations within individual platforms, this study shows that coordination frequently transcends platform boundaries. Leveraging newly collected data of online conversations related to the 2024 U.S. Election across X (formerly, Twitter), Facebook, and Telegram
By Marco Minici
[from the abstract] Information Operations (IOs) pose a significant threat to the integrity of democratic processes, with the potential to influence election-related online discourse. In anticipation of the 2024 U.S. presidential election, we present a study aimed at uncovering the digital traces of coordinated IOs on X (formerly Twitter). Using our machine learning framework for detecting online coordination, we analyze a dataset comprising election-related conversations on X from May 2024.
By Leonardo Blas
This repository contains the largest public Telegram dataset to date, providing an unparalleled opportunity to study political discussions on Telegram in the lead-up to the 2024 U.S. elections. The dataset will be regularly updated with additional data to enhance ongoing research. Stay tuned for future updates!
By Ashwin Balasubramanian
This repository contains a large-scale dataset capturing discourse on X (formerly known as Twitter) related to the upcoming 2024 U.S. Presidential Election and the dynamics of political engagement on social media. The dataset will be continuously updated with additional scrapes. Stay tuned!
By Kashish Shah
This repository is a comprehensive dataset capturing activity on TruthSocial related to the upcoming 2024 US Presidential Election, including posts, comments and user interactions. This dataset comprises 1.5 million posts published between February 2022 and October 2024
By Dr. Emilio Ferrara
Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) pose significant risks, particularly in the realm of online election interference.
This paper explores the nefarious applications of GenAI, highlighting their potential to disrupt democratic processes through deepfakes, botnets, targeted misinformation campaigns, and synthetic identities.
By Emily Chen
[from the abstract]…We decided to publicly release a massive-scale, longitudinal dataset of U.S. politics- and election-related tweets. This multilingual dataset that we have been collecting for over one year encompasses hundreds of millions of tweets and tracks all salient U.S. politics trends, actors, and events between 2019 and 2020…
By Dr. Luca Luceri
[from the abstract]…The aftermath of the 2020 US Presidential Election witnessed an unprecedented attack on the democratic values of the country through the violent insurrection at Capitol Hill on January 6th, 2021. The attack was fueled by the proliferation of conspiracy theories and misleading claims about the integrity of the election pushed by political elites and fringe communities on social media.
In this study, we explore the evolution of fringe content and conspiracy theories on Twitter in the seven months leading up to the Capitol attack.
The 2024 Election Integrity Initiative 