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Flekova, Lucie, Jordan Carpenter, Salvatore Giorgi, Lyle Ungar, and Daniel Preoţiuc-Pietro. Analysing Biases in Human Perception of User Age and Gender from Text. ACL., 2016. AbstractPDFPoster

User traits disclosed through written text, such as age and gender, can be used to personalize applications such as recommender systems or conversational agents. However, human perception of these traits is not perfectly aligned with reality. In this paper, we conduct a large-scale crowdsourcing experiment on guessing age and gender from tweets. We systematically analyze the quality and possible biases of these predictions. We identify the textual cues which lead to miss-assessments of traits or make workers more or less confident in their choice. Our study demonstrates that differences between real and perceived traits are noteworthy and elucidates inaccurately used stereotypes in human perception.

Flekova, Lucie, Eugen Ruppert, and Daniel Preotiuc-Pietro. Analysing domain suitability of a sentiment lexicon by identifying distributionally bipolar words In Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA). EMNLP, 2015. AbstractPDFSlides

Contemporary sentiment analysis approaches rely heavily on lexicon based methods. This is mainly due to their simplicity, although the best empirical results can be achieved by more complex techniques. We introduce a method to assess suitability of generic sentiment lexicons for a given domain, namely to identify frequent bigrams where a polar word switches polarity. Our bigrams are scored using Lexicographers Mutual Information and leveraging large automatically obtained corpora. Our score matches human perception of polarity and demonstrates improvements in classification results using our enhanced context-aware method. Our method enhances the assessment of lexicon based sentiment detection algorithms and can be further used to quantify ambiguous words.

Preoţiuc-Pietro, Daniel, Vasileios Lampos, and Nikolaos Aletras. An analysis of the user occupational class through Twitter content In ACL., 2015. AbstractPDFSlides

Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the prediction of the occupational class for a public user profile. Our analysis is conducted on a new annotated corpus of Twitter users, their respective job titles, posted textual content and platform-related attributes. We frame our task as classification using latent feature representations such as word clusters and embeddings. The employed linear and, especially, non-linear methods can predict a user’s occupational class with strong accuracy for the coarsest level of a standard occupation taxonomy which includes nine classes. Combined with a qualitative assessment, the derived results confirm the feasibility of our approach in inferring a new user attribute that can be embedded in a multitude of downstream applications.

Flekova, Lucie, Daniel Preoţiuc-Pietro, Jordan Carpenter, Salvatore Giorgi, and Lyle Ungar. Analyzing crowdsourced assessment of user traits through Twitter posts In Work-in-Progress. HCOMP, 2015. AbstractPDFSup. MaterialsPoster

Social media allows any user to express themselves to the public through posting content. Using a crowdsourcing experiment, we aim to quantify and analyze which human attributes lead to better perceptions of the true identity of others. Using tweet content from a set of users with known age and gender information, we ask workers to rate their perception of these traits and we analyze those results in relation to the crowdsourcing workers’ age and gender. Results show that female workers are both more confident and more accurate at reporting gender, and workers in their thirties were most accurate but least confident for rating age. Our study is a first step in identifying the worker traits which contribute to a better understanding of others through their posted text content. Our findings help to identify the types of workers best suited for certain tasks.

Leqi, Liu, Daniel Preoţiuc-Pietro, Zahra Riahi, Mohsen E. Moghaddam, and Lyle Ungar. Analyzing Personality through Social Media Profile Picture Choice In ICWSM., 2016. AbstractPDFSlides

The content of images users post to their social media is driven in part by personality. In this study, we analyze how Twitter profile images vary with the personality of the users posting them. In our main analysis, we use profile images from over 66,000 users whose personality we estimate based on their tweets. To facilitate interpretability, we focus our analysis on aesthetic and facial features and control for demographic variation in image features and personality. Our results show significant differences in profile picture choice between personality traits, and that these can be harnessed to predict personality traits with robust accuracy. For example, agreeable and conscientious users display more positive emotions in their profile pictures, while users high in openness prefer more aesthetic photos.