An Exploratory Analysis of the Relation between Offensive Language and Mental Health

Bucur, Ana-Maria; Zampieri, Marcos; Dinu, Liviu P.

doi:10.18653/v1/2021.findings-acl.315

Cited by 12 publications

(7 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Birnbaum et al (2020) found that depressed individuals use more swear words in their Facebook messages compared to controls. Bucur et al (2021b) apply offensive language identification techniques and show that users with depression diagnosis use more offensive language in their Reddit posts, individuals manifesting signs of depression in their posts having a more profane language and fewer insults targeted towards other individuals or groups.…”

Section: Related Workmentioning

confidence: 99%

A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media

Bucur

Podină

Dinu

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing M

Self Cite

View full text Add to dashboard Cite

In this work, we provide an extensive part-ofspeech analysis of the discourse of social media users with depression. Research in psychology revealed that depressed users tend to be self-focused, more preoccupied with themselves and ruminate more about their lives and emotions. Our work aims to make use of largescale datasets and computational methods for a quantitative exploration of discourse. We use the publicly available depression dataset from the Early Risk Prediction on the Internet Workshop (eRisk) 2018 and extract partof-speech features and several indices based on them. Our results reveal statistically significant differences between the depressed and non-depressed individuals confirming findings from the existing psychology literature. Our work provides insights regarding the way in which depressed individuals are expressing themselves on social media platforms, allowing for better-informed computational models to help monitor and prevent mental illnesses.

show abstract

Section: Related Workmentioning

confidence: 99%

A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media

Bucur

Podină

Dinu

2021

Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing M

Self Cite

View full text Add to dashboard Cite

show abstract

“…Even if many works exploring the social media discourse of people diagnosed with depression (Orabi et al, 2018;Burdisso et al, 2019;Uban and Rosso, 2020;Bucur et al, 2021c) are paying attention to the emotions expressed in their social media discourse (Aragon et al, 2021;Lara et al, 2021;Howes et al, 2014), to the best of our knowledge, works focusing on happiness felt by individuals diagnosed with depression are missing. Therefore, we aim to fill this gap and explore the happy moments from the online discourse of users with depression in comparison with control users.…”

Section: Related Workmentioning

confidence: 99%

Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression

Bucur¹,

Cosma²,

Dinu³

2022

Preprint

Self Cite

View full text Add to dashboard Cite

In this work, we explore the relationship between depression and manifestations of happiness in social media. While the majority of works surrounding depression focus on symptoms, psychological research shows that there is a strong link between seeking happiness and being diagnosed with depression. We make use of Positive-Unlabeled learning paradigm to automatically extract happy moments from social media posts of both controls and users diagnosed with depression, and qualitatively analyze them with linguistic tools such as LIWC and keyness information. We show that the life of depressed individuals is not always bleak, with positive events related to friends and family being more noteworthy to their lives compared to the more mundane happy events reported by control users.

show abstract

“…OLID dataset It was the official dataset of the SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval 2019) (Zampieri et al, 2019b) and SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020) (Zampieri et al, 2020). The dataset was also used in misogyny (Pamungkas et al, 2020), cyberbullying (Aind et al, 2020) and depression (Bucur et al, 2021b) research. It contains 14,100 tweets with a hierarchical annotation taxonomy with three levels: Level A -Offensive language identification (offensive vs non-offensive), Level B -categorization of Offensive language (targeted insults or threats vs untargeted profanity) and Level C -Offensive language target identification (individual vs group vs other).…”

Section: Language Example Raw Example Goldmentioning

confidence: 99%

Sequence-to-Sequence Lexical Normalization with Multilingual Transformers

Bucur¹,

Cosma²,

Dinu³

2021

Proceedings of the Seventh Workshop on Noisy User-Generated Text (W-Nut 2021)

Self Cite

View full text Add to dashboard Cite

Current benchmark tasks for natural language processing contain text that is qualitatively different from the text used in informal day to day digital communication. This discrepancy has led to severe performance degradation of state-of-the-art NLP models when fine-tuned on real-world data. One way to resolve this issue is through lexical normalization, which is the process of transforming non-standard text, usually from social media, into a more standardized form. In this work, we propose a sentence-level sequence-to-sequence model based on mBART, which frames the problem as a machine translation problem. As the noisy text is a pervasive problem across languages, not just English, we leverage the multilingual pre-training of mBART to fine-tune it to our data. While current approaches mainly operate at the word or subword level, we argue that this approach is straightforward from a technical standpoint and builds upon existing pre-trained transformer networks. Our results show that while word-level, intrinsic, performance evaluation is behind other methods, our model improves performance on extrinsic, downstream tasks through normalization compared to models operating on raw, unprocessed, social media text.

show abstract

An Exploratory Analysis of the Relation between Offensive Language and Mental Health

Cited by 12 publications

References 39 publications

A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media

A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media

Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression

Sequence-to-Sequence Lexical Normalization with Multilingual Transformers

Contact Info

Product

Resources

About