mlsanityData to 7 Oct 2026

Analyzing Online Political Advertisements

arXiv 2105.04047 ↗First posted 9 May 2021cs.CLcited by 11

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Abstract (arXiv)

Online political advertising is a central aspect of modern election campaigning for influencing public opinion. Computational analysis of political ads is of utmost importance in political science to understand the characteristics of digital campaigning. It is also important in computational linguistics to study features of political discourse and communication on a large scale. In this work, we present the first computational study on online political ads with the aim to (1) infer the political ideology of an ad sponsor; and (2) identify whether the sponsor is an official political party or a third-party organization. We develop two new large datasets for the two tasks consisting of ads from the U.S.. Evaluation results show that our approach that combines textual and visual information from pre-trained neural models outperforms a state-of-the-art method for generic commercial ad classification. Finally, we provide an in-depth analysis of the limitations of our best-performing models and linguistic analysis to study the characteristics of political ads discourse.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 74 references that are in the map. First posted on arXiv 9 May 2021.

Builds on

24 of 74 references.

References · 24

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12 more
  1. 2017ADVISE: Symbolism and External Knowledge for Decoding Advertisementscited by 21
  2. 2018Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Textcited by 15
  3. 2020Analyzing Political Parody in Social Mediacited by 13
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  5. 2018Topic-Specific Sentiment Analysis Can Help Identify Political Ideologycited by 9
  6. 2019Understanding the Political Ideology of Legislators from Social Media Imagescited by 9
  7. 2018Story Understanding in Video Advertisementscited by 5
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  12. 2019An Analysis of United States Online Political Advertising Transparencycited by 1

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Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 3; 2024: 1; 2025: 2.

11 papers.

  1. 2021Detecting Dementia from Speech and Transcripts using Transformerscited by 13
  2. 2021Point-of-Interest Type Prediction using Text and Imagescited by 10
  3. 2022Combining Humor and Sarcasm for Improving Political Parody Detectioncited by 6
  4. 2022Identifying and Characterizing Active Citizens who Refute Misinformation in Social Mediacited by 5
  5. 2022PAR: Political Actor Representation Learning with Social Context and Expert Knowledgecited by 5
  6. 2023Context-aware attention layers coupled with optimal transport domain adaptation and multimodal fusion methods for recognizing dementia from spontaneous speechcited by 5
  7. 2025Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understandingcited by 3
  8. 2023A Multimodal Analysis of Influencer Content on Twittercited by 2
  9. 2023Improving Multimodal Classification of Social Media Posts by Leveraging Image-Text Auxiliary Taskscited by 2
  10. 2025On VLMs for Diverse Tasks in Multimodal Meme Classificationcited by 2
  11. 2024Alignment Helps Make the Most of Multimodal Datacited by 1