A5007955173- Topic Modeling
- Misinformation and Its Impacts
- Deception detection and forensic psychology
- Natural Language Processing Techniques
- Mental Health via Writing
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Authorship Attribution and Profiling
- Psychopathy, Forensic Psychiatry, Sexual Offending
- Cybercrime and Law Enforcement Studies
- Emotion and Mood Recognition
- Digital Mental Health Interventions
- Hate Speech and Cyberbullying Detection
- Speech and dialogue systems
- Spam and Phishing Detection
- Health Literacy and Information Accessibility
- Social Media in Health Education
- Diabetes Management and Research
- Color perception and design
- Diabetes and associated disorders
- Metabolism, Diabetes, and Cancer
- Information and Cyber Security
- Language, Metaphor, and Cognition
- Complex Network Analysis Techniques
- Humor Studies and Applications
Texas State University
2025
University of Michigan
2015-2024
Group for the Analysis of Development
2023
Singapore University of Technology and Design
2019-2022
Philipps University of Marburg
2022
University of North Carolina Health Care
2020
East Stroudsburg University
2020
University of North Carolina at Chapel Hill
2020
University of Notre Dame
2020
Japan Society
2019
The proliferation of misleading information in everyday access media outlets such as social feeds, news blogs, and online newspapers have made it challenging to identify trustworthy sources, thus increasing the need for computational tools able provide insights into reliability content. In this paper, we focus on automatic identification fake content news. Our contribution is twofold. First, introduce two novel datasets task detection, covering seven different domains. We describe...
Hearings of witnesses and defendants play a crucial role when reaching court trial decisions. Given the high-stake nature outcomes, implementing accurate effective computational methods to evaluate honesty testimonies can offer valuable support during decision making process. In this paper, we address identification deception in real-life data. We introduce novel dataset consisting videos collected from public trials. explore use verbal non-verbal modalities build multimodal detection system...
Santiago Castro, Devamanyu Hazarika, Verónica Pérez-Rosas, Roger Zimmermann, Rada Mihalcea, Soujanya Poria. Proceedings of the 57th Annual Meeting Association for Computational Linguistics. 2019.
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The widespread use of deception in online sources has motivated the need for methods to automatically profile and identify deceivers.This work explores deception, gender age detection short texts using a machine learning approach.First, we collect new open domain dataset also containing demographic data such as age.Second, extract feature sets including n-grams, shallow deep syntactic features, semantic complexity readability metrics.Third, build classifiers that aim predict gender, age.Our...
Deception detection has been receiving an increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One major challenges encountered in this task is availability data, most research work to date conducted on acted or artificially collected data. The generated deception models are thus lacking real-world evidence. In paper, we explore use real-life data for detection. We develop a new dataset consisting videos reallife scenarios, build...
Deception detection has received an increasing amount of attention in recent years, due to the significant growth digital media, as well increased ethical and security concerns. Earlier approaches deception were mainly focused on law enforcement applications relied polygraph tests, which had proved falsely accuse innocent free guilty multiple cases. In this paper, we explore a multimodal approach that relies novel data set 149 recordings, integrates physiological, linguistic, thermal...
Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An. Proceedings of the 55th Annual Meeting Association for Computational Linguistics (Volume 1: Long Papers). 2017.
In this paper, we address the task of cross-cultural deception detection.Using crowdsourcing, collect three datasets, two in English (one originating from United States and one India), Spanish obtained speakers Mexico.We run comparative experiments to evaluate accuracies classifiers built for each culture, also analyze classification differences within across cultures.Our results show that can leverage information, either through translation or equivalent semantic categories, build with a...
In this paper we address the automatic identification of deceit by using a multimodal approach. We collect deceptive and truthful responses setting where acquire data microphone, thermal camera, as well physiological sensors. Among all available modalities, focus on three modalities namely, language use, response, sensing. To our knowledge, is first work to integrate these specific detect deceit. Several experiments are carried out in which select representative features for each modality,...
The quality of a counseling intervention relies highly on the active collaboration between clients and counselors. In this paper, we explore several linguistic aspects process occurring during conversations. Specifically, address differences high-quality low-quality counseling. Our approach examines participants' turn-by-turn interaction, their alignment, sentiment expressed by speakers conversation, as well different topics being discussed. results suggest important language in low-...
Recent years have witnessed a significant increase in the online sharing of medical information, with videos representing large fraction such sources. Previous studies however shown that more than half health-related on platforms as YouTube contain misleading information and biases. Hence, it is crucial to build computational tools can help evaluate quality these so users obtain accurate inform their decisions. In this study, we focus automatic detection misinformation videos. We select...
Hearings of witnesses and defendants play a crucial role when reaching court trial decisions. Given the high-stakes nature outcomes, developing computational models that assist decision-making process is an important research venue. In this article, we address identification deception in real-life data. We use dataset consisting videos collected from public trials. explore verbal non-verbal modalities to build multimodal detection system aims discriminate between truthful deceptive...
Verónica Pérez-Rosas, Rada Mihalcea, Kenneth Resnicow, Satinder Singh, Lawrence An, Kathy J. Goggin, Delwyn Catley. Proceedings of the 15th Conference European Chapter Association for Computational Linguistics: Volume 1, Long Papers. 2017.
Felix Soldner, Verónica Pérez-Rosas, Rada Mihalcea. Proceedings of the 2019 Conference North American Chapter Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Cultural and language factors significantly influence counseling, but Natural Language Processing research has not yet examined whether the findings of conversational analysis for counseling conducted in English apply to other languages. This paper presents a first step towards this direction. We introduce MIDAS (Motivational Interviewing Dataset Spanish), dataset created from public video sources that contains expert annotations reflections questions. Using dataset, we explore...