Detecting Anxiety through Reddit

Shen, Judy Hanwen; Rudzicz, Frank

doi:10.18653/v1/w17-3107

Cited by 175 publications

(122 citation statements)

References 14 publications

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“…Dunlop et al [10] show that online discussion forums were associated with increases in suicidal ideation while Luxton et al [19] show that social media can pose a threat to some vulnerable groups of society. Some uni-modal studies [21,27] have examined behavioral markers of depression in social media by leveraging the posts on Reddit and Twitter.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Behavioral Markers Exploring Suicidal Intent in Social Media Videos

Shah

Vaibhav

Sharma

et al. 2019

2019 International Conference on Multimodal Interaction

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Suicide is one of the leading causes of death in the modern world. In this digital age, individuals are increasingly using social media to express themselves and often use these platforms to express suicidal intent. Various studies have inspected suicidal intent behavioral markers in controlled environments but it is still unexplored if such markers will generalize to suicidal intent expressed on social media. In this work, we set out to study multimodal behavioral markers related to suicidal intent when expressed on social media videos. We explore verbal, acoustic and visual behavioral markers in the context of identifying individuals at higher risk of suicidal attempt. Our analysis reveals that frequent silences, slouched shoulders, rapid hand movements and profanity are predominant multimodal behavioral markers indicative of suicidal intent 1 .

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Section: Related Workmentioning

confidence: 99%

Multimodal Behavioral Markers Exploring Suicidal Intent in Social Media Videos

Shah

Vaibhav

Sharma

et al. 2019

2019 International Conference on Multimodal Interaction

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“…Wallace et al (2014) tackled irony detection and concluded that Reddit provides a lot of context, which can help in dealing with the ambiguous cases. Shen and Rudzicz (2017) achieved good results in anxiety classification using the Linguistic Inquiry and Word Count (LIWC) dictionary (Pennebaker et al, 2003), n-grams, and topic modeling. To the best of our knowledge, ours it the first work on using Reddit as a source of data for personality prediction.…”

Section: Background and Related Workmentioning

confidence: 99%

Reddit: A Gold Mine for Personality Prediction

Gjurković¹,

Šnajder²

2018

Proceedings of the Second Workshop on Computational Modeling Of People’s Opinions, Personality, and Emotions in Socia

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Automated personality prediction from social media is gaining increasing attention in natural language processing and social sciences communities. However, due to high labeling costs and privacy issues, the few publicly available datasets are of limited size and low topic diversity. We address this problem by introducing a large-scale dataset derived from Reddit, a source so far overlooked for personality prediction. The dataset is labeled with Myers-Briggs Type Indicators (MBTI) and comes with a rich set of features for more than 9k users. We carry out a preliminary feature analysis, revealing marked differences between the MBTI dimensions and poles. Furthermore, we use the dataset to train and evaluate benchmark personality prediction models, achieving macro F1-scores between 67% and 82% on the individual dimensions and 82% accuracy for exact or one-off accurate type prediction. These results are encouraging and comparable with the reliability of standardized tests.

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“…Wallace et al (2014) tackled irony detection and concluded that Reddit provides a lot of context, which can help in dealing with the ambiguous cases. Shen and Rudzicz (2017) achieved good results in anxiety classification using the Linguistic Inquiry and Word Count (LIWC) dictionary , n-grams, and topic modeling. To the best of our knowledge, ours it the first work on using Reddit as a source of data for personality prediction.…”

Section: Background and Related Workmentioning

confidence: 99%

Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

2018

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The idea of organizing PEOPLES stemmed from two related observations, namely the availability of large amounts of spontaneous data covering a range of personal aspects and the fact that such aspects are usually studied in isolation. Social media users nowadays freely express what is on their mind at any moment in time, at any location, and about virtually anything. These large amounts of spontaneously produced texts open up a unique opportunity to learn more about such users, e.g., predicting demographic variables (age, gender), but also personality types, as well as emotions and opinion expressions. This observation is not new, of course, and this opportunity has largely been exploited in the recent years, with abundant works on sentiment analysis, emotion detection, and personality. However, such traits of human personality and behavior have indeed attracted a substantial amount of attention but have been mostly studied in isolation, often in different -but related -communities, such as NLP, CL, AI. Therefore, we thought that the time was ripe to bring these communities a step closer to study people's traits and expressions jointly and in their interplay on such large volumes of available data.The communities' response, with 25 received submissions coming from 11 different countries and going well beyond typical NLP topics, proves again this year that there is wide interest at this intersection, and we are happy to be able to provide a context for exchanging ideas.Following the reviewers's advice, 14 papers were selected for inclusion in the proceedings. They cover a wide range of topics related to the three main PEOPLES themes (personality, emotion and opinion), their interaction and the impact of their modeling on social aspects like well-being, political preferences, humor and language use.To further enrich this volume, we additionally invited our keynote speakers to submit position papers that accompany their talks, and are excited that both of our keynotes submitted excellent papers touching upon issues of making NLP models more demographically aware and how researchers from related fields such as demography can benefit from NLP techniques.We hope that this is just the second edition of what will become series of workshops bringing together researchers in Computational Linguistics, Natural Language Processing and Computational Social Science, who share an interest in personality, opinion and emotion detection, and especially in researching the intertwining of such traits and expressions.We would like to thank our program committee consisting of 33 researchers from a variety of backgrounds for their insightful and constructive reviews. Without their support, this workshop would not have been possible. In addition, we thank all authors for submitting papers and making PEOPLES a big success. Also thanks to our two invited speakers, Dirk Hovy and Letizia Mencarini (Bocconi University, Italy), for having accepted to come to the workshop and share their expertise and ideas on PEOPLES' topics. We thank NAACL for...

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Detecting Anxiety through Reddit

Cited by 175 publications

References 14 publications

Multimodal Behavioral Markers Exploring Suicidal Intent in Social Media Videos

Multimodal Behavioral Markers Exploring Suicidal Intent in Social Media Videos

Reddit: A Gold Mine for Personality Prediction

Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

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