• Publications
  • Influence
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
TLDR
Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter
TLDR
This work builds predictive models to classify 130 thousand news posts as suspicious or verified, and predict four sub-types of suspicious news – satire, hoaxes, clickbait and propaganda, and shows that neural network models trained on tweet content and social network interactions outperform lexical models.
Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media
TLDR
It is shown that gender differences in subjective language can effectively be used to improve sentiment analysis, and in particular, polarity classification for Spanish and Russian.
RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian
TLDR
RuSentiment, a new dataset for sentiment analysis of social media posts in Russian, and a new set of comprehensive annotation guidelines that are extensible to other languages are presented.
Inferring Latent User Properties from Texts Published in Social Media
TLDR
This work examines individual tweets to detect emotions and opinions emanating from them, and analyzes all the tweets published by a user to infer latent traits of that individual, focusing on Ekman's six emotions.
Inferring User Political Preferences from Streaming Communications
TLDR
It is found that political preference can be often be predicted using roughly 100 tweets, depending on the context of user selection, where this could mean hours, or weeks, based on the author’s tweeting frequency.
On Predicting Sociodemographic Traits and Emotions from Communications in Social Networks and Their Implications to Online Self-Disclosure
TLDR
It is found that some users tend to express significantly more joy and significantly less sadness in their tweets, such as those predicted to be in a relationship, with children, or with a higher than average annual income or educational level.
Inferring Perceived Demographics from User Emotional Tone and User-Environment Emotional Contrast
TLDR
The network structure is explored and it is shown that it is possible to accurately predict a range of perceived demographic traits based solely on the emotions emanating from users and their neighbors.
Identifying Effective Signals to Predict Deleted and Suspended Accounts on Twitter Across Languages
TLDR
This work is the first to rely on image and affect signals in addition to language and network to predict deleted and suspended accounts in social media and finds that unlike widely used profile and network features, the discourse of deleted or suspended versus active accounts forms the basis for highly accurate account deletion and suspension prediction.
Fishing for Clickbaits in Social Images and Texts with Linguistically-Infused Neural Network Models
TLDR
This work is the first to present preliminary analysis of objects extracted using Google Tensorflow object detection API from images in clickbait vs. non-clickbait Twitter posts, and it is not found that image representations combined with text yield significant performance improvement yet.
...
...