The Social Impact of Natural Language Processing

Hovy, Dirk; Spruit, Shannon

doi:10.18653/v1/p16-2096

Cited by 246 publications

(229 citation statements)

References 38 publications

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“…Ethical considerations are established as essential part in planning mental health research and most research projects undergo approval by an ethics committee. On the contrary, the computational linguistics community has started only recently to consider ethical questions (Hovy and Spruit, 2016;Hovy et al, 2017). Likely, this is because computational linguistics was traditionally concerned with publicly available, impersonal texts such as newspapers or texts published with some temporal distance, which left a distance between the text and author.…”

Section: Ethical Considerationsmentioning

confidence: 99%

“…Likely, this is because computational linguistics was traditionally concerned with publicly available, impersonal texts such as newspapers or texts published with some temporal distance, which left a distance between the text and author. Conversely, recent social media research often deals with highly personal information of living individuals, who can be directly affected by the outcomes (Hovy and Spruit, 2016).…”

Section: Ethical Considerationsmentioning

confidence: 99%

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A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

Jagfeld

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery -living a satisfying and contributing life along symptoms of severe mental health issues -so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries. Complementary to this evidence, computational linguistic methods allow us to analyse firstperson accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.

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Section: Ethical Considerationsmentioning

confidence: 99%

Section: Ethical Considerationsmentioning

confidence: 99%

A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

Jagfeld

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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“…At the same time, demographic factors do create a demographic bias in the training data that influences NLP tools like POS taggers (Jørgensen et al, 2015;Hovy and Søgaard, 2015), leading to possible exclusion of under-represented demographic groups (Hovy and Spruit, 2016). Current methods, however, still fail to explicitly account for such biases, and can in fact even increase the demographic bias (Zhao et al, 2017).…”

Section: Debiasing and Other Applicationsmentioning

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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“…As recently noted by Hovy and Spruit (2016), there has been "little discourse in the [NLP] community" about ethics, and the social impact of natural language processing. Their paper opens up the discussion, and provides some useful terminology, which can be summarized as follows:…”

Section: Bias and Ethicsmentioning

confidence: 99%

Pragmatic descriptions of perceptual stimuli

Miltenburg¹

2017

Proceedings of the Student Research Workshop at the 15th Conference Of the European Chapter of the Association for Co

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This research proposal discusses pragmatic factors in image description, arguing that current automatic image description systems do not take these factors into account. I present a general model of the human image description process, and propose to study this process using corpus analysis, experiments, and computational modeling. This will lead to a better characterization of human image description behavior, providing a road map for future research in automatic image description, and the automatic description of perceptual stimuli in general.

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The Social Impact of Natural Language Processing

Cited by 246 publications

References 38 publications

A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

A Computational Linguistic Study of Personal Recovery in Bipolar Disorder

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

Pragmatic descriptions of perceptual stimuli

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