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Found 8 papers, 3 papers with code
On the Reliability and Validity of Detecting Approval of Political Actors in Tweets
Various sentiment analysis and stance detection methods have been developed and used in previous research to measure users{'} political opinions based on their content on social media.
The Unseen Targets of Hate -- A Systematic Review of Hateful Communication Datasets
Yet, ML tools can only be as capable as the quality of the data they are trained on allows them.
People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection
CADs introduce minimal changes to existing training data points and flip their labels; training on them may reduce model dependency on spurious features.
Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection
Especially, construct-driven CAD -- perturbations of core features -- may induce models to ignore the context in which core features are used.
"Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples
By providing a scale-based codebook and insights regarding the shortcomings of the state-of-the-art, we hope to contribute to the development of better and broader models for sexism detection, including reflections on theory-driven approaches to data collection.
TED-On: A Total Error Framework for Digital Traces of Human Behavior on Online Platforms
While there are clear parallels to the well-known error sources in the TSE framework, the new "Total Error Framework for Digital Traces of Human Behavior on Online Platforms" (TED-On) identifies several types of error that are specific to the use of digital traces.
Computers and Society Human-Computer Interaction Social and Information Networks
A Twitter Corpus for Hindi-English Code Mixed POS Tagging
Code-mixing is a linguistic phenomenon where multiple languages are used in the same occurrence that is increasingly common in multilingual societies.
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets
While growing code-mixed content on Online Social Networks(OSN) provides a fertile ground for studying various aspects of code-mixing, the lack of automated text analysis tools render such studies challenging.
