Papers by Ankit Aich
Telling a Lie: Analyzing the Language of Information and Misinformation during Global Health Events (2022.lrec-1)
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| Challenge: | a new dataset is available to stimulate research on health misinformation . linguistic characteristics of health misinfonia are unique to COVID-19 and other events . |
| Approach: | They propose a new dataset that analyzes health misinformation at scale . it includes 2.8 million news articles and social media posts covering diseases . authors propose an annotation framework that allows for strong agreement between annotators . |
| Outcome: | The proposed dataset is based on 2.8 million news articles and social media posts spanning 1900s to present . it shows that the proposed model is robust and can be used to detect misinformation . |
Modeling Human Subjectivity in LLMs Using Explicit and Implicit Human Factors in Personas (2024.findings-emnlp)
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Salvatore Giorgi, Tingting Liu, Ankit Aich, Kelsey Isman, Garrick Sherman, Zachary Fried, João Sedoc, Lyle Ungar, Brenda Curtis
| Challenge: | Large language models (LLMs) are increasingly being used in human-centered social scientific tasks, such as data annotation, synthetic data creation, and engaging in dialog. |
| Approach: | They propose to prompt LLMs with human-like personas and ask them to answer as if they were a specific human, either explicitly, with exact demographics, political beliefs, and lived experiences, or implicitly via names prevalent in specific populations. |
| Outcome: | The proposed model is based on explicit, explicit, and implicit personas, and fails to show implicit biases. |
TweetTaglish: A Dataset for Investigating Tagalog-English Code-Switching (2022.lrec-1)
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| Challenge: | a large dataset is available to study Tagalog-English code-switching in low-resource settings. |
| Approach: | They propose to use a large dataset to investigate Tagalog-English code-switching . they use linguistic data from Tagalogue and Tagalit-English to investigate their results . |
| Outcome: | The proposed dataset achieves a strong performance benchmark for Tagalog-English code-switching. |
Towards Intelligent Clinically-Informed Language Analyses of People with Bipolar Disorder and Schizophrenia (2022.findings-emnlp)
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| Challenge: | Existing studies on social media data have limited the extent to which they can produce meaningful or generalizable conclusions. |
| Approach: | They propose to use transcribed conversations with people with bipolar disorder and schizophrenia to create a large dataset of transcriptions. |
| Outcome: | The proposed dataset extracts 100+ temporal, sentiment, psycholinguistic, emotion, and lexical features and establishes classification validity. |
Towards Comprehensive Language Analysis for Clinically Enriched Spontaneous Dialogue (2024.lrec-main)
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| Challenge: | Contemporary NLP has progressed from feature-based classification to fine-tuning and prompt-based techniques . many of these techniques remain understudied in the context of real-world, clinically enriched spontaneous dialogue. |
| Approach: | They investigate the efficacy and overall performance of a range of NLP techniques on transcribed speech from patients with schizophrenia and other disorders. |
| Outcome: | The proposed methods are effective in analyzing transcribed speech from patients with schizophrenia and healthy controls taking a clinically-validated language test. |
Demystifying Neural Fake News via Linguistic Feature-Based Interpretation (2022.coling-1)
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| Challenge: | Recent advances to neural fake news generators have made it difficult to understand how misinformation generated by these models may best be confronted. |
| Approach: | They conduct feature-based analysis to gain an interpretative understanding of the linguistic attributes that neural fake news generators may most effectively exploit. |
| Outcome: | The proposed models are compared with models trained on subsets of features and confronted with increasingly advanced neural fake news. |