{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T16:28:12Z","timestamp":1780590492884,"version":"3.54.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030454418","type":"print"},{"value":"9783030454425","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-45442-5_33","type":"book-chapter","created":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T21:03:47Z","timestamp":1586552627000},"page":"265-271","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Utilizing Temporal Psycholinguistic Cues for Suicidal Intent Estimation"],"prefix":"10.1007","author":[{"given":"Puneet","family":"Mathur","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ramit","family":"Sawhney","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivang","family":"Chopra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maitree","family":"Leekha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajiv","family":"Ratn Shah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"key":"33_CR1","doi-asserted-by":"crossref","unstructured":"Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using Twitter users\u2019 psychological features and machine learning. Comput. Secur. 101710 (2020)","DOI":"10.1016\/j.cose.2019.101710"},{"key":"33_CR2","doi-asserted-by":"crossref","unstructured":"Balani, S., De Choudhury, M.: Detecting and characterizing mental health related self-disclosure in social media. In: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1373\u20131378. ACM (2015)","DOI":"10.1145\/2702613.2732733"},{"key":"33_CR3","unstructured":"Benton, A., Mitchell, M., Hovy, D.: Multi-task learning for mental health using social media text. arXiv preprint arXiv:1712.03538 (2017)"},{"key":"33_CR4","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.chb.2015.08.023","volume":"54","author":"PA Cavazos-Rehg","year":"2016","unstructured":"Cavazos-Rehg, P.A., et al.: A content analysis of depression-related tweets. Comput. Hum. Behav. 54, 351\u2013357 (2016)","journal-title":"Comput. Hum. Behav."},{"key":"33_CR5","doi-asserted-by":"crossref","unstructured":"Cero, I., Witte, T.K.: Assortativity of suicide-related posting on social media. Am. Psychol. (2019)","DOI":"10.1037\/amp0000477"},{"key":"33_CR6","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/978-981-15-2021-1_12","volume-title":"Intelligence Enabled Research","author":"A Chatterjee","year":"2020","unstructured":"Chatterjee, A., Das, A.: Temporal sentiment analysis of the data from social media to early detection of cyberbullicide ideation of a victim by using graph-based approach and data mining tools. In: Bhattacharyya, S., Mitra, S., Dutta, P. (eds.) Intelligence Enabled Research. AISC, vol. 1109, pp. 107\u2013112. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-2021-1_12"},{"key":"33_CR7","unstructured":"De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Seventh International AAAI Conference on Weblogs and Social Media (2013)"},{"key":"33_CR8","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"33_CR9","doi-asserted-by":"publisher","first-page":"104540","DOI":"10.1016\/j.jmva.2019.104540","volume":"175","author":"KH Lee","year":"2020","unstructured":"Lee, K.H., Xue, L., Hunter, D.R.: Model-based clustering of time-evolving networks through temporal exponential-family random graph models. J. Multivar. Anal. 175, 104540 (2020)","journal-title":"J. Multivar. Anal."},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Lopez-Castroman, J., et al.: Mining social networks to improve suicide prevention: a scoping review. J. Neurosci. Res. (2019)","DOI":"10.1002\/jnr.24404"},{"key":"33_CR11","doi-asserted-by":"crossref","unstructured":"Mathur, P., Sawhney, R., Shah, R.R.: Suicide risk assessment via temporal psycholinguistic modeling (student abstract). In: 2020 Proceedings of the 34th AAAI Conference on Artificial Intelligence. AAAI (2020)","DOI":"10.1609\/aaai.v34i10.7209"},{"key":"33_CR12","doi-asserted-by":"crossref","unstructured":"Mathur, P., Shah, R., Sawhney, R., Mahata, D.: Detecting offensive tweets in Hindi-English code-switched language. In: Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media, pp. 18\u201326 (2018)","DOI":"10.18653\/v1\/W18-3504"},{"key":"33_CR13","unstructured":"Mishra, P., Del Tredici, M., Yannakoudakis, H., Shutova, E.: Author profiling for abuse detection. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1088\u20131098 (2018)"},{"key":"33_CR14","unstructured":"Mishra, R., Sinha, P.P., Sawhney, R., Mahata, D., Mathur, P., Shah, R.R.: SNAP-BATNET: cascading author profiling and social network graphs for suicide ideation detection on social media. In: Proceedings of the 2019 NAACL Student Research Workshop, pp. 147\u2013156 (2019)"},{"issue":"10","key":"33_CR15","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1089\/tmj.2018.0203","volume":"25","author":"A Pourmand","year":"2019","unstructured":"Pourmand, A., Roberson, J., Caggiula, A., Monsalve, N., Rahimi, M., Torres-Llenza, V.: Social media and suicide: a review of technology-based epidemiology and risk assessment. Telemed. e-Health 25(10), 880\u2013888 (2019)","journal-title":"Telemed. e-Health"},{"key":"33_CR16","doi-asserted-by":"crossref","unstructured":"Qian, J., ElSherief, M., Belding, E.M., Wang, W.Y.: Leveraging intra-user and inter-user representation learning for automated hate speech detection. arXiv preprint arXiv:1804.03124 (2018)","DOI":"10.18653\/v1\/N18-2019"},{"key":"33_CR17","doi-asserted-by":"crossref","unstructured":"Sawhney, R., Manchanda, P., Mathur, P., Shah, R., Singh, R.: Exploring and learning suicidal ideation connotations on social media with deep learning. In: Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 167\u2013175 (2018)","DOI":"10.18653\/v1\/W18-6223"},{"key":"33_CR18","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.future.2019.08.022","volume":"102","author":"B Steer","year":"2020","unstructured":"Steer, B., Cuadrado, F., Clegg, R.: Raphtory: streaming analysis of distributed temporal graphs. Future Gener. Comput. Syst. 102, 453\u2013464 (2020)","journal-title":"Future Gener. Comput. Syst."},{"issue":"1","key":"33_CR19","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3390\/a13010007","volume":"13","author":"MM Tadesse","year":"2020","unstructured":"Tadesse, M.M., Lin, H., Xu, B., Yang, L.: Detection of suicide ideation in social media forums using deep learning. Algorithms 13(1), 7 (2020)","journal-title":"Algorithms"},{"key":"33_CR20","unstructured":"Zhou, P., Qi, Z., Zheng, S., Xu, J., Bao, H., Xu, B.: Text classification improved by integrating bidirectional lstm with two-dimensional max pooling. arXiv preprint arXiv:1611.06639 (2016)"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45442-5_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:26:50Z","timestamp":1710358010000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-45442-5_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030454418","9783030454425"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45442-5_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"8 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2020.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"457","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"55","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"46","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"12% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Also included: 8 reproducibility papers, 10 demonstration papers, 12 CLEF organizers lab track papers, 7 doctoral consortium papers, 4 workshops, 3 tutorials. Due to the COVID-19 pandemic, this conference was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}