ArticleScientific reports2021
Using sentiment analysis to predict opinion inversion in Tweets of political communication.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- A machine learning based empathy mapping framework for enhancing user experience through app review analysis.Scientific reports · 2025Article
- Highly engaging events reveal semantic and temporal compression in online community discourse.PNAS nexus · 2025Article
- Emoji multimodal microblog sentiment analysis based on mutual attention mechanism.Scientific reports · 2024Article
- Political uses of the ancient past on social media are predominantly negative and extreme.PloS one · 2024Article
- On the use of sentiment analysis for linguistics research. Observations on sentiment polarity and the use of the progressive in Italian.Frontiers in artificial intelligence · 2023Article
- Pooches on a platform: Text mining twitter for sector perceptions of dogs during a global pandemic.Frontiers in veterinary science · 2023Article
- Mining Public Opinions on COVID-19 Vaccination: A Temporal Analysis to Support Combating Misinformation.Tropical medicine and infectious disease · 2022Article
- Authorship identification using ensemble learning.Scientific reports · 2022Article
- Grass-roots entrepreneurship complements traditional top-down innovation in lung and breast cancer.NPJ digital medicine · 2022Article
- Emotions and virality: Social transmission of political messages on Twitter.Frontiers in psychology · 2022Article
- Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China.Computational urban science · 2022Article
- Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation.Sensors (Basel, Switzerland) · 2021Article
- An investigation into the deep learning approach in sentimental analysis using graph-based theories.PloS one · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social media networks have become an essential tool for sharing information in political discourse. Recent studies examining opinion diffusion have highlighted that some users may invert a message's content before disseminating it, propagating a contrasting view relative to that of the original author. Using politically-oriented discourse related to Israel with focus on the Israeli-Palestinian conflict, we explored this Opinion Inversion (O.I.) phenomenon. From a corpus of approximately 716,000 relevant Tweets, we identified 7147 Source-Quote pairs. These Source-Quote pairs accounted for 69% of the total volume of the corpus. Using a Random Forest model based on the Natural Language Processing features of the Source text and user attributes, we could predict whether a Source will undergo O.I. upon retweet with an ROC-AUC of 0.83. We found that roughly 80% of the factors that explain O.I. are associated with the original message's sentiment towards the conflict. In addition, we identified pairs comprised of Quotes related to the domain while their Sources were unrelated to the domain. These Quotes, which accounted for 14% of the Source-Quote pairs, maintained similar sentiment levels as the Source. Our case study underscores that O.I. plays an important role in political communication on social media. Nevertheless, O.I. can be predicted in advance using simple artificial intelligence tools and that prediction might be used to optimize content propagation.
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Registered trials
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.