research-article

Public Access

Understanding and Discovering Deliberate Self-harm Content in Social Media

Authors:

Clayton Mellina,

Yi ChangAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 93 - 102

https://doi.org/10.1145/3038912.3052555

Published: 03 April 2017 Publication History

Abstract

Studies suggest that self-harm users found it easier to discuss self-harm-related thoughts and behaviors using social media than in the physical world. Given the enormous and increasing volume of social media data, on-line self-harm content is likely to be buried rapidly by other normal content. To enable voices of self-harm users to be heard, it is important to distinguish self-harm content from other types of content. In this paper, we aim to understand self-harm content and provide automatic approaches to its detection. We first perform a comprehensive analysis on self-harm social media using different input cues. Our analysis, the first of its kind in large scale, reveals a number of important findings. Then we propose frameworks that incorporate the findings to discover self-harm content under both supervised and unsupervised settings. Our experimental results on a large social media dataset from Flickr demonstrate the effectiveness of the proposed frameworks and the importance of our findings in discovering self-harm content.

References

[1]

S. Chancellor, Z. J. Lin, and M. D. Choudhury. "this post will just get taken down": Characterizing removed pro-eating disorder social media content. In CHI, San Jose, CA, USA, May 7-12, 2016.

Digital Library

[2]

A. L. Chapman, K. L. Gratz, and M. Z. Brown. Solving the puzzle of deliberate self-harm: The experiential avoidance model. Behaviour research and therapy, 44(3):371--394, 2006.

[3]

X. Chen and K. S. Candan. Lwi-svd: low-rank, windowed, incremental singular value decompositions on time-evolving data sets. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 987--996. ACM, 2014.

Digital Library

[4]

M.-M. Cheng, N. J. Mitra, X. Huang, P. H. Torr, and S.-M. Hu. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(3):569--582, 2015.

Digital Library

[5]

J. Cohen. Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. Psychological bulletin, 70(4):213, 1968.

[6]

K. Daine, K. Hawton, V. Singaravelu, A. Stewart, S. Simkin, and P. Montgomery. The power of the web: a systematic review of studies of the influence of the internet on self-harm and suicide in young people. PloS one, 8(10):e77555, 2013.

[7]

M. De Choudhury, M. Gamon, S. Counts, and E. Horvitz. Predicting depression via social media. In ICWSM, page 2, 2013.

[8]

M. De Choudhury, E. Kiciman, M. Dredze, G. Coppersmith, and M. Kumar. Discovering shifts to suicidal ideation from mental health content in social media. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pages 2098--2110. ACM, 2016.

Digital Library

[9]

C. H. Ding, T. Li, and M. I. Jordan. Convex and semi-nonnegative matrix factorizations. IEEE transactions on pattern analysis and machine intelligence, 32(1):45--55, 2010.

Digital Library

[10]

M. P. Dyson, L. Hartling, J. Shulhan, A. Chisholm, A. Milne, P. Sundar, S. D. Scott, and A. S. Newton. A systematic review of social media use to discuss and view deliberate self-harm acts. PLoS one, 11(5):e0155813, 2016.

[11]

C. Eichenberg, J. Dyba, and M. Schott. Bindungsstile, nutzungsmotive und internetsucht. Psychiatrische Praxis, 44(01):41--46, 2017.

[12]

K. Gimpel, N. Schneider, B. O'Connor, D. Das, D. Mills, J. Eisenstein, M. Heilman, D. Yogatama, J. Flanigan, and N. A. Smith. Part-of-speech tagging for twitter: Annotation, features, and experiments. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers-Volume 2, pages 42--47. Association for Computational Linguistics, 2011.

Digital Library

[13]

L. A. Goodman. Snowball sampling. The annals of mathematical statistics, pages 148--170, 1961.

[14]

K. L. Gratz, S. D. Conrad, and L. Roemer. Risk factors for deliberate self-harm among college students. American journal of Orthopsychiatry, 72(1):128, 2002.

[15]

K. Hawton, J. Fagg, S. Simkin, E. Bale, and A. Bond. Trends in deliberate self-harm in oxford, 1985--1995. implications for clinical services and the prevention of suicide. The British Journal of Psychiatry, 171(6):556--560, 1997.

[16]

K. Hawton and A. James. Suicide and deliberate self harm in young people. Bmj, 330(7496):891--894, 2005.

[17]

K. Hawton, K. Rodham, E. Evans, and R. Weatherall. Deliberate self harm in adolescents: self report survey in schools in england. Bmj, 325(7374):1207--1211, 2002.

[18]

K. Hawton, K. E. Saunders, and R. C. O'Connor. Self-harm and suicide in adolescents. The Lancet, 379(9834):2373--2382, 2012.

[19]

D. J. Houghton and A. N. Joinson. Linguistic markers of secrets and sensitive self-disclosure in twitter. In System Science (HICSS), 2012 45th Hawaii International Conference on, pages 3480--3489. IEEE, 2012.

Digital Library

[20]

L. Itti and C. Koch. Computational modelling of visual attention. Nature reviews neuroscience, 2(3):194--203, 2001.

[21]

S. Kairam, J. Kaye, J. A. G. Gómez, and D. A. Shamma. Snap decisions? how users, content, and aesthetics interact to shape photo sharing behaviors. CHI 2016, 2016.

Digital Library

[22]

Y. Kim. Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882, 2014.

[23]

A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097--1105, 2012.

Digital Library

[24]

X. Li, S. Huang, K. S. Candan, and M. L. Sapino. Focusing decomposition accuracy by personalizing tensor decomposition (PTD). In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, CIKM 2014, Shanghai, China, November 3-7, 2014, pages 689--698, 2014.

Digital Library

[25]

W. Lian, P. Rai, E. Salazar, and L. Carin. Integrating features and similarities: flexible models for heterogeneous multiview data. In Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pages 2757--2763. AAAI Press, 2015.

Digital Library

[26]

L. Lustberg and C. F. Reynolds. Depression and insomnia: questions of cause and effect. Sleep medicine reviews, 4(3):253--262, 2000.

[27]

J. Machajdik and A. Hanbury. Affective image classification using features inspired by psychology and art theory. In Proceedings of the 18th ACM international conference on Multimedia, pages 83--92. ACM, 2010.

Digital Library

[28]

T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.

[29]

M. A. Milkie. Social comparisons, reflected appraisals, and mass media: The impact of pervasive beauty images on black and white girls' self-concepts. Social Psychology Quarterly, pages 190--210, 1999.

[30]

M. A. Moreno, A. Ton, E. Selkie, and Y. Evans. Secret society 123: understanding the language of self-harm on instagram. Journal of Adolescent Health, 58(1):78--84, 2016.

[31]

J. J. Muehlenkamp, L. Claes, L. Havertape, and P. L. Plener. International prevalence of adolescent non-suicidal self-injury and deliberate self-harm. Child and Adolescent Psychiatry and Mental Health, 6(1):1, 2012.

[32]

J. P. Pestian, P. Matykiewicz, M. Linn-Gust, B. South, O. Uzuner, J. Wiebe, K. B. Cohen, J. Hurdle, and C. Brew. Sentiment analysis of suicide notes: A shared task. Biomedical informatics insights, 5(Suppl. 1):3, 2012.

[33]

K. Petrie and R. Brook. Sense of coherence, self-esteem, depression and hopelessness as correlates of reattempting suicide. British Journal of Clinical Psychology, 31(3):293--300, 1992.

[34]

J. Robinson, G. Cox, E. Bailey, S. Hetrick, M. Rodrigues, S. Fisher, and H. Herrman. Social media and suicide prevention: a systematic review. Early Interv Psychiatry, 2015.

[35]

S. S. Rude, C. R. Valdez, S. Odom, and A. Ebrahimi. Negative cognitive biases predict subsequent depression. Cognitive Therapy and Research, 27(4):415--429, 2003.

[36]

A. Sadilek, H. A. Kautz, and V. Silenzio. Modeling spread of disease from social interactions. 2012.

[37]

S. Siersdorfer, E. Minack, F. Deng, and J. Hare. Analyzing and predicting sentiment of images on the social web. In Proceedings of the 18th ACM international conference on Multimedia, pages 715--718. ACM, 2010.

Digital Library

[38]

S. W. Stirman and J. W. Pennebaker. Word use in the poetry of suicidal and nonsuicidal poets. Psychosomatic Medicine, 63(4):517--522, 2001.

[39]

B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li. The new data and new challenges in multimedia research. arXiv preprint arXiv:1503.01817, 2015.

[40]

T. Tuytelaars and K. Mikolajczyk. Local invariant feature detectors: a survey. Foundations and trends® in computer graphics and vision, 3(3):177--280, 2008.

Digital Library

[41]

N. X. Vinh, J. Epps, and J. Bailey. Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance. JLMR, pages 2837--2854, 2010.

Digital Library

[42]

S. Wang, Y. Wang, J. Tang, C. Aggarwal, S. Ranganath, and H. Liu. Exploiting hierarchical structures for unsupervised feature selection. In Proceedings of the 2017 SIAM International Conference on Data Mining, 2017.

[43]

S. Wang, Y. Wang, J. Tang, K. Shu, S. Ranganath, and H. Liu. What your images reveal: Exploiting visual contents for point-of-interest recommendation. In Proceedings of the 26th International Conference on World Wide Web, 2017.

Digital Library

[44]

Y. Wang, Y. Hu, S. Kambhampati, and B. Li. Inferring sentiment from web images with joint inference on visual and social cues: A regulated matrix factorization approach. In Ninth International AAAI Conference on Web and Social Media, 2015.

[45]

Y. Wang and B. Li. Sentiment analysis for social media images. In Proceedings of the 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pages 1584--1591. IEEE Computer Society, 2015.

Digital Library

[46]

Y. Wang, S. Wang, J. Tang, H. Liu, and B. Li. Unsupervised sentiment analysis for social media images. In Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015.

Digital Library

[47]

Y. Wang, S. Wang, J. Tang, H. Liu, and B. Li. Ppp: Joint pointwise and pairwise image label prediction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6005--6013, 2016.

[48]

J. M. G. Williams and M. Williams. Cry of pain: Understanding suicide and self-harm. Penguin Group USA, 1997.

[49]

E. Yom-Tov, L. Fernandez-Luque, I. Weber, and S. P. Crain. Pro-anorexia and pro-recovery photo sharing: a tale of two warring tribes. Journal of medical Internet research, 14(6):e151, 2012.

Cited By

Atauri-Mezquida DNogales-González CMartínez-Pastor E(2025)Exploring self-harm on Twitter (X): Content moderation and its psychological effects on adolescentsOnline Journal of Communication and Media Technologies10.30935/ojcmt/1586715:1(e202503)Online publication date: 2025
https://doi.org/10.30935/ojcmt/15867
Nova FShinde APfafman RHarrison ALogan Delaney CPater J(2025)Sentiment Analysis of #Meanspo Tweets: Humans vs. Automatic ClassificationsProceedings of the ACM on Human-Computer Interaction10.1145/37012079:1(1-26)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701207
Nova FPfafman RLogan Delaney CPater J(2024)Unveiling the "Toxic" World of #Meanspo: Understanding Users' Emerging Online Eating Disorder Practices in X/TwitterProceedings of the ACM on Human-Computer Interaction10.1145/36870618:CSCW2(1-27)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687061
Show More Cited By

Index Terms

Understanding and Discovering Deliberate Self-harm Content in Social Media
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
      1. User studies

Recommendations

Understanding social media marketing: a case study on topics, categories and sentiment on a Facebook brand page
MindTrek '11: Proceedings of the 15th International Academic MindTrek Conference: Envisioning Future Media Environments

Social networks have changed the way information is delivered to the customers, shifting from traditional one-to-many to one-to-one communication. Opinion mining and sentiment analysis offer the possibility to understand the user-generated comments and ...
Detecting and Characterizing Mental Health Related Self-Disclosure in Social Media
CHI EA '15: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems

Self-disclosure is an important element facilitating improved psychological wellbeing in individuals with mental illness. As social media is increasingly adopted in health related discourse, we examine how these new platforms might be allowing honest ...
Discovering Overlapping Groups in Social Media
ICDM '10: Proceedings of the 2010 IEEE International Conference on Data Mining

The increasing popularity of social media is shortening the distance between people. Social activities, e.g., tagging in Flickr, book marking in Delicious, twittering in Twitter, etc. are reshaping people’s social life and redefining their social roles. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

ARO
ONR

Conference

WWW '17

Sponsor:

IW3C2

WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

Acceptance Rates

WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

33
Total Citations
View Citations
1,745
Total Downloads

Downloads (Last 12 months)293
Downloads (Last 6 weeks)37

Reflects downloads up to 28 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Atauri-Mezquida DNogales-González CMartínez-Pastor E(2025)Exploring self-harm on Twitter (X): Content moderation and its psychological effects on adolescentsOnline Journal of Communication and Media Technologies10.30935/ojcmt/1586715:1(e202503)Online publication date: 2025
https://doi.org/10.30935/ojcmt/15867
Nova FShinde APfafman RHarrison ALogan Delaney CPater J(2025)Sentiment Analysis of #Meanspo Tweets: Humans vs. Automatic ClassificationsProceedings of the ACM on Human-Computer Interaction10.1145/37012079:1(1-26)Online publication date: 10-Jan-2025
https://dl.acm.org/doi/10.1145/3701207
Nova FPfafman RLogan Delaney CPater J(2024)Unveiling the "Toxic" World of #Meanspo: Understanding Users' Emerging Online Eating Disorder Practices in X/TwitterProceedings of the ACM on Human-Computer Interaction10.1145/36870618:CSCW2(1-27)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687061
Wang BSun YZi YZhao YQin B(2024)Scale-CoT: Integrating LLM with Psychiatric Scales for Analyzing Mental Health Issues on Social Media2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)10.1109/BIBM62325.2024.10822322(2651-2658)Online publication date: 3-Dec-2024
https://doi.org/10.1109/BIBM62325.2024.10822322
Bethany MSeong ASilva SBeebe NVishwamitra NNajafirad PCalandrino JTroncoso C(2023)Towards targeted obfuscation of adversarial unsafe images using reconstruction and counterfactual super region attribution explainabilityProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620274(643-660)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620274
Farajzadeh NSadeghzadeh N(2023)NSSI questionnaires revisited: A data mining approach to shorten the NSSI questionnairesPLOS ONE10.1371/journal.pone.028458818:4(e0284588)Online publication date: 21-Apr-2023
https://doi.org/10.1371/journal.pone.0284588
Tuarob SChatrinan KNoraset TTawichsri TThaipisutikul T(2023)Forecasting National-Level Self-Harm Trends With Social NetworksIEEE Access10.1109/ACCESS.2023.328929511(64796-64814)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3289295
Garg M(2023)Mental Health Analysis in Social Media Posts: A SurveyArchives of Computational Methods in Engineering10.1007/s11831-022-09863-z30:3(1819-1842)Online publication date: 3-Jan-2023
https://doi.org/10.1007/s11831-022-09863-z
Fukazawa Y(2022)Estimating Mental Health Using Human-generated Big Data and Machine Learning人が生み出すビッグデータと機械学習によるメンタルヘルスの推定The Brain & Neural Networks10.3902/jnns.29.7829:2(78-94)Online publication date: 5-Jun-2022
https://doi.org/10.3902/jnns.29.78
Datta SKollnig KShadbolt N(2022)Mind-proofing Your Phone: Navigating the Digital Minefield with GreaseTerminatorProceedings of the 27th International Conference on Intelligent User Interfaces10.1145/3490099.3511152(523-536)Online publication date: 22-Mar-2022
https://dl.acm.org/doi/10.1145/3490099.3511152
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Figures

Tables

Media

View Table of Conten