Related papers: Public Reaction to Scientific Research via Twitter Sentiment Prediction

Related papers

Modeling Public Perceptions of Science in Media [49.096529873255385]
We introduce a computational framework that models public perception across twelve dimensions, such as newsworthiness, importance, and surprisingness.<n>We create a large-scale science news perception dataset with 10,489 annotations from 2,101 participants from diverse US and UK populations.<n>We develop NLP models that predict public perception scores with a strong performance.
arXiv Detail & Related papers (2025-06-19T21:49:28Z)
Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently. Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z)
Web Scraping for Research: Legal, Ethical, Institutional, and Scientific Considerations [11.851771490297693]
This paper proposes a comprehensive framework for web scraping in social science research for U.S.-based researchers. We present an overview of the current regulatory environment impacting when and how researchers can access, collect, store, and share data via scraping. We then provide researchers with recommendations to conduct scraping in a scientifically legitimate and ethical manner.
arXiv Detail & Related papers (2024-10-30T20:20:44Z)
Understanding Fine-grained Distortions in Reports of Scientific Findings [46.96512578511154]
Distorted science communication harms individuals and society as it can lead to unhealthy behavior change and decrease trust in scientific institutions. Given the rapidly increasing volume of science communication in recent years, a fine-grained understanding of how findings from scientific publications are reported to the general public is crucial.
arXiv Detail & Related papers (2024-02-19T19:00:01Z)
Forecasting high-impact research topics via machine learning on evolving knowledge graphs [0.8158530638728501]
We show how to predict the impact of onsets of ideas that have never been published by researchers. We developed a large evolving knowledge graph built from more than 21 million scientific papers.
arXiv Detail & Related papers (2024-02-13T18:09:38Z)
Human-Centered Responsible Artificial Intelligence: Current & Future Trends [76.94037394832931]
In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. All of this work is aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends.
arXiv Detail & Related papers (2023-02-16T08:59:42Z)
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets [0.7850663096185592]
The COVID-19 pandemic is one of the current events being discussed on social media platforms. In a pandemic situation, analyzing social media texts to uncover sentimental trends can be very helpful. We intend to study society's perception of the COVID-19 pandemic through social media using state-of-the-art BERT and Deep CNN models.
arXiv Detail & Related papers (2022-11-04T14:35:56Z)
Who Shares Fake News? Uncovering Insights from Social Media Users' Post Histories [0.0]
We propose that social-media users' own post histories are an underused resource for studying fake-news sharing. We identify cues that distinguish fake-news sharers, predict those most likely to share fake news, and identify promising constructs to build interventions.
arXiv Detail & Related papers (2022-03-20T14:26:20Z)
What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses [58.75684238003408]
psycho-linguistics analyses of social media text are vital for drawing meaningful conclusions to mitigate misinformation. This research contributes by performing an in-depth psycholinguistic analysis of rumours related to various kinds of events.
arXiv Detail & Related papers (2021-11-09T07:45:11Z)
A Study on Herd Behavior Using Sentiment Analysis in Online Social Network [1.5673338088641469]
This paper represents and analyze the capacity of diverse strategies to predict critical opinions from online social networking sites. Social media becomes a good outlet since the last decades to share the opinions globally. This study demonstrates the evaluation of sentiment analysis techniques using social media contents.
arXiv Detail & Related papers (2021-07-25T05:22:35Z)
A Measure of Research Taste [91.3755431537592]
We present a citation-based measure that rewards both productivity and taste. The presented measure, CAP, balances the impact of publications and their quantity. We analyze the characteristics of CAP for highly-cited researchers in biology, computer science, economics, and physics.
arXiv Detail & Related papers (2021-05-17T18:01:47Z)
Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
We argue that it is critical to understand what user attributes potentially cause users to share fake news. In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. We propose a principled approach to alleviating selection bias in fake news dissemination.
arXiv Detail & Related papers (2020-10-20T19:37:04Z)
Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z)
Measuring the Diversity of Facebook Reactions to Research [0.0]
We present a novel way of weighting click-based reactions that increases their comprehensibility. We use our metrics along with LDA topic models and statistical testing to investigate how users' emotional responses differ from one scientific topic to another. We find that there is generally a positive response to scientific research on Facebook, and that articles generating a positive emotional response are more likely to be widely shared.
arXiv Detail & Related papers (2020-01-04T03:41:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.