Weakly Supervised Learning for Analyzing Political Campaigns on Facebook
arXiv:2210.10669 · doi:10.1609/icwsm.v17i1.22156
Abstract
Social media platforms are currently the main channel for political messaging, allowing politicians to target specific demographics and adapt based on their reactions. However, making this communication transparent is challenging, as the messaging is tightly coupled with its intended audience and often echoed by multiple stakeholders interested in advancing specific policies. Our goal in this paper is to take a first step towards understanding these highly decentralized settings. We propose a weakly supervised approach to identify the stance and issue of political ads on Facebook and analyze how political campaigns use some kind of demographic targeting by location, gender, or age. Furthermore, we analyze the temporal dynamics of the political ads on election polls.
accepted at 17th International AAAI Conference on Web and Social Media (ICWSM-2023), 12 pages
References in corpus (7)
- LINE: Large-scale Information Network Embedding
- CLEAR: Contrastive Learning for Sentence Representation
- Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook
- Clandestino or Rifugiato? Anti-immigration Facebook Ad Targeting in Italy
- Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision
- Facebook Ad Engagement in the Russian Active Measures Campaign of 2016
- Weakly Supervised Learning of Nuanced Frames for Analyzing Polarization in News Media