Analyzing Online Political Advertisements
- URL: http://arxiv.org/abs/2105.04047v1
- Date: Sun, 9 May 2021 23:18:37 GMT
- Title: Analyzing Online Political Advertisements
- Authors: Danae S\'anchez Villegas, Saeid Mokaram, Nikolaos Aletras
- Abstract summary: We present the first computational study on online political ads with the aim to infer the political ideology of an ad sponsor.
We develop two new large datasets for the two tasks consisting of ads from the U.S.
- Score: 10.386018392170083
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 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 a linguistic analysis to study the characteristics of political ads
discourse.
Related papers
- On the Use of Proxies in Political Ad Targeting [49.61009579554272]
We show that major political advertisers circumvented mitigations by targeting proxy attributes.
Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
arXiv Detail & Related papers (2024-10-18T17:15:13Z) - Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
This study seeks to identify and quantify biases in simulating political samples with Large Language Models.
Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
arXiv Detail & Related papers (2024-07-16T05:52:26Z) - Political advertisement on Facebook and Instagram in the run up to 2022
Italian general election [0.9496529663479973]
We study the extent to which political ads were delivered on Facebook and Instagram in the run up to 2022 Italian general election.
We analyze over 23 k unique ads paid by 2.7 k unique sponsors, with an associated amount spent of 4 M EUR and over 1 billion views generated.
We find results that are in accordance with their political agenda and the electoral outcome.
arXiv Detail & Related papers (2022-12-12T13:37:18Z) - Weakly Supervised Learning for Analyzing Political Campaigns on Facebook [24.29993132301275]
We propose a weakly supervised approach to identify the stance and issue of political ads on Facebook.
We analyze the temporal dynamics of the political ads on election polls.
arXiv Detail & Related papers (2022-10-19T15:35:37Z) - PAR: Political Actor Representation Learning with Social Context and
Expert Knowledge [45.215862050840116]
We propose textbfPAR, a textbfPolitical textbfActor textbfRepresentation learning framework.
We retrieve and extract factual statements about legislators to leverage social context information.
We then construct a heterogeneous information network to incorporate social context and use relational graph neural networks to learn legislator representations.
arXiv Detail & Related papers (2022-10-15T19:28:06Z) - Persuasion Strategies in Advertisements [68.70313043201882]
We introduce an extensive vocabulary of persuasion strategies and build the first ad image corpus annotated with persuasion strategies.
We then formulate the task of persuasion strategy prediction with multi-modal learning.
We conduct a real-world case study on 1600 advertising campaigns of 30 Fortune-500 companies.
arXiv Detail & Related papers (2022-08-20T07:33:13Z) - How Algorithms Shape the Distribution of Political Advertising: Case
Studies of Facebook, Google, and TikTok [5.851101657703105]
We analyze a dataset containing over 800,000 ads and 2.5 million videos about the 2020 U.S. presidential election from Facebook, Google, and TikTok.
We conduct the first large scale data analysis of public data to critically evaluate how these platforms amplified or moderated the distribution of political advertisements.
We conclude with recommendations for how to improve the disclosures so that the public can hold the platforms and political advertisers accountable.
arXiv Detail & Related papers (2022-06-09T18:19:30Z) - Political Posters Identification with Appearance-Text Fusion [49.55696202606098]
We propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters.
The majority of this work focuses on political posters that are designed to serve as a promotion of a certain political event.
arXiv Detail & Related papers (2020-12-19T16:14:51Z) - 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) - Inferring Political Preferences from Twitter [0.0]
Political Sentiment Analysis of social media helps the political strategists to scrutinize the performance of a party or candidate.
During the time of elections, the social networks get flooded with blogs, chats, debates and discussions about the prospects of political parties and politicians.
In this work, we chose to identify the inclination of political opinions present in Tweets by modelling it as a text classification problem using classical machine learning.
arXiv Detail & Related papers (2020-07-21T05:20:43Z) - A Dip Into a Deep Well: Online Political Advertisements, Valence, and
European Electoral Campaigning [0.7106986689736826]
The paper examines online political ads by using a dataset collected from Google's transparency reports.
According to the results, most of the political ads have expressed positive sentiments.
arXiv Detail & Related papers (2020-01-28T22:33:45Z)
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.