Evidence map›Paper›PMID 40737276›Full record

ArticlePloS one2025

Investigating the impact of social media images on users' sentiments towards sociopolitical events based on deep artificial intelligence.

Nafiseh Jabbari Tofighi, Reda Alhajj

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Nafiseh Jabbari TofighiDepartment of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey.
Reda AlhajjDepartment of Computer Engineering, Istanbul Medipol University, Istanbul, Turkey.ORCID https://orcid.org/0000-0001-6657-9738

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents the findings of the research aimed at investigating the influence of visual content, posted on social media in shaping users' sentiments towards specific sociopolitical events. The study analyzed various sociopolitical topics by examining posts containing relevant hashtags and keywords, along with their associated images and comments. Using advanced machine learning and deep learning methods for sentiment analysis, textual data were classified to determine the expressed sentiments. Additionally, the correlation between posted visual content and user sentiments has been studied. A particular emphasis was placed on understanding how these visuals impact users' attitudes toward the events. The research resulted in a comprehensive dataset comprising labeled images and their comments, offering valuable insights into the dynamics of public opinion formation through social media. This study investigates the influence of social media images on user sentiment toward sociopolitical events using deep learning-based sentiment analysis. By analyzing posts from movements such as Black Lives Matter, Women's March, Climate Change Protests, and Anti-war Demonstrations, we identified a strong correlation between visual content and public sentiment. Our results reveal that Anti-war Demonstrations exhibit the highest correlation (PLCC: 0.709, SROCC: 0.723), while Climate Change Protests display the lowest alignment (PLCC: 0.531, SROCC: 0.611). Overall, the study finds a consistent positive correlation (PLCC range: 0.615-0.709, SROCC: 0.611-0.723) across movements, indicating the significant role of visual content in shaping the public opinion.

Indexed as

Artificial IntelligenceDeep LearningPoliticsPublic OpinionSocial MediaFemaleHumans

Identifiers

PMID40737276
PMCPMC12310001

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.