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Found 21 papers, 7 papers with code
Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity
Knowledge graphs play a pivotal role in various applications, such as question-answering and fact-checking.
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Graph Matching
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ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?
Our results show that ChatGPT aligns better with humans in more coarse-grained scales.
Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance
We investigate the performance of LLM-based zero-shot stance detection on tweets.
Token-Ensemble Text Generation: On Attacking the Automatic AI-Generated Text Detection
The robustness of AI-content detection models against cultivated attacks (e. g., paraphrasing or word switching) remains a significant concern.
Can we trust the evaluation on ChatGPT?
ChatGPT, the first large language model (LLM) with mass adoption, has demonstrated remarkable performance in numerous natural language tasks.
Wearing Masks Implies Refuting Trump?: Towards Target-specific User Stance Prediction across Events in COVID-19 and US Election 2020
People who share similar opinions towards controversial topics could form an echo chamber and may share similar political views toward other topics as well.
Is ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech
Recent studies have alarmed that many online hate speeches are implicit.
Chain of Explanation: New Prompting Method to Generate Higher Quality Natural Language Explanation for Implicit Hate Speech
Recent studies have exploited advanced generative language models to generate Natural Language Explanations (NLE) for why a certain text could be hateful.
Who Is Missing? Characterizing the Participation of Different Demographic Groups in a Korean Nationwide Daily Conversation Corpus
A conversation corpus is essential to build interactive AI applications.
Understanding Toxicity Triggers on Reddit in the Context of Singapore
While the contagious nature of online toxicity sparked increasing interest in its early detection and prevention, most of the literature focuses on the Western world.
A Survey on Predicting the Factuality and the Bias of News Media
The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically.
How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement
To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary.
What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context
Alternatively, we can profile entire news outlets and look for those that are likely to publish fake or biased content.
A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017
By examining the ebb and flow of media frames over almost two decades, we show that short-term frame abundance fluctuation closely corresponds to major events, while there also exist several long-term trends, such as the gradually increasing prevalence of the ``Cultural identity'' frame.
"Trust me, I have a Ph.D.": A Propensity Score Analysis on the Halo Effect of Disclosing One's Offline Social Status in Online Communities
The results show that exposing academic degrees is likely to lead to higher audience votes as well as larger discussion size, compared to the users without the disclosed identities, in a community that covers peer-reviewed scientific articles.
Social and Information Networks
FrameAxis: Characterizing Microframe Bias and Intensity with Word Embedding
{Here, we propose FrameAxis, a method for characterizing documents by identifying the most relevant semantic axes ("microframes") that are overrepresented in the text using word embedding.
Tanbih: Get To Know What You Are Reading
We introduce Tanbih, a news aggregator with intelligent analysis tools to help readers understanding what's behind a news story.
Political Discussions in Homogeneous and Cross-Cutting Communication Spaces
While research on political talk in online environments abounds, we know surprisingly little about the potentially varying nature of discussions in politically homogeneous spaces as compared to cross-cutting communication spaces.
SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment
Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge.
Revealing the Hidden Patterns of News Photos: Analysis of Millions of News Photos Using GDELT and Deep Learning-based Vision APIs
In this work, we analyze more than two million news photos published in January 2016.
Breaking the News: First Impressions Matter on Online News
In order to discover strategies that could be used to attract clicks, we extracted features from the text of the news headlines related to the sentiment polarity of the headline.
