Search Results for author: Sarah Ita Levitan

Found 11 papers, 2 papers with code

Improving Cross-domain, Cross-lingual and Multi-modal Deception Detection

no code implementations ACL 2022 Subhadarshi Panda, Sarah Ita Levitan

With the increase of deception and misinformation especially in social media, it has become crucial to be able to develop machine learning methods to automatically identify deceptive language.

Classification Deception Detection +2

A Novel Methodology for Developing Automatic Harassment Classifiers for Twitter

1 code implementation EMNLP (ALW) 2020 Ishaan Arora, Julia Guo, Sarah Ita Levitan, Susan McGregor, Julia Hirschberg

Most efforts at identifying abusive speech online rely on public corpora that have been scraped from websites using keyword-based queries or released by site or platform owners for research purposes.

Detecting Multilingual COVID-19 Misinformation on Social Media via Contextualized Embeddings

1 code implementation NAACL (NLP4IF) 2021 Subhadarshi Panda, Sarah Ita Levitan

We present machine learning classifiers to automatically identify COVID-19 misinformation on social media in three languages: English, Bulgarian, and Arabic.


Acoustic-Prosodic and Lexical Cues to Deception and Trust: Deciphering How People Detect Lies

no code implementations TACL 2020 Xi (Leslie) Chen, Sarah Ita Levitan, Michelle Levine, M, Marko ic, Julia Hirschberg

We analyzed the acoustic-prosodic and linguistic characteristics of language trusted and mistrusted by raters and compared these to characteristics of actual truthful and deceptive language to understand how perception aligns with reality.

Deception Detection

Linguistic Analysis of Schizophrenia in Reddit Posts

no code implementations WS 2019 Jonathan Zomick, Sarah Ita Levitan, Mark Serper

In this paper we leverage the vast amount of data available from social media and use statistical and machine learning approaches to study linguistic characteristics of SZ.

BIG-bench Machine Learning

Comparing Approaches for Automatic Question Identification

no code implementations SEMEVAL 2017 Angel Maredia, Kara Schechtman, Sarah Ita Levitan, Julia Hirschberg

Collecting spontaneous speech corpora that are open-ended, yet topically constrained, is increasingly popular for research in spoken dialogue systems and speaker state, inter alia.

Semantic Textual Similarity Spoken Dialogue Systems +1

Cannot find the paper you are looking for? You can Submit a new open access paper.