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Found 9 papers, 1 papers with code
Analyzing the Effects of Annotator Gender across NLP Tasks
Recent studies have shown that for subjective annotation tasks, the demographics, lived experiences, and identity of annotators can have a large impact on how items are labeled.
Misinformation as Information Pollution
Social media feed algorithms are designed to optimize online social engagements for the purpose of maximizing advertising profits, and therefore have an incentive to promote controversial posts including misinformation.
Has It All Been Solved? Open NLP Research Questions Not Solved by Large Language Models
Not surprisingly, this has, in turn, made many NLP researchers -- especially those at the beginning of their careers -- worry about what NLP research area they should focus on.
Query Rewriting for Effective Misinformation Discovery
We propose a novel system to help fact-checkers formulate search queries for known misinformation claims and effectively search across multiple social media platforms.
Matching Tweets With Applicable Fact-Checks Across Languages
We conduct both classification and retrieval experiments, in monolingual (English only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English) settings using multilingual transformer models such as XLM-RoBERTa and multilingual embeddings such as LaBSE and SBERT.
Tiplines to Combat Misinformation on Encrypted Platforms: A Case Study of the 2019 Indian Election on WhatsApp
There is currently no easy way to fact-check content on WhatsApp and other end-to-end encrypted platforms at scale.
Claim Matching Beyond English to Scale Global Fact-Checking
We train our own embedding model using knowledge distillation and a high-quality "teacher" model in order to address the imbalance in embedding quality between the low- and high-resource languages in our dataset.
Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News
In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications.
Biased TextRank: Unsupervised Graph-Based Content Extraction
We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input "focus."
