{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T07:13:44Z","timestamp":1770966824317,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T00:00:00Z","timestamp":1702166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T00:00:00Z","timestamp":1702166400000},"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":["Soc. Netw. Anal. Min."],"DOI":"10.1007\/s13278-023-01167-9","type":"journal-article","created":{"date-parts":[[2023,12,10]],"date-time":"2023-12-10T06:01:17Z","timestamp":1702188077000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Incorporating historical information by disentangling hidden representations for mental health surveillance on social media"],"prefix":"10.1007","volume":"14","author":[{"given":"Usman","family":"Naseem","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Surendrabikram","family":"Thapa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junaid","family":"Rashid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mehwish","family":"Nasim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,12,10]]},"reference":[{"key":"1167_CR1","doi-asserted-by":"crossref","unstructured":"Adhikari S, Thapa S, Singh P, Huo H, Bharathy G, Prasad M (2021) A comparative study of machine learning and nlp techniques for uses of stop words by patients in diagnosis of Alzheimer\u2019s disease. In: 2021 International joint conference on neural networks (IJCNN). IEEE, pp 1\u20138","DOI":"10.1109\/IJCNN52387.2021.9534449"},{"key":"1167_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijhcs.2021.102761","volume":"160","author":"S Adhikari","year":"2022","unstructured":"Adhikari S, Thapa S, Naseem U, Singh P, Huo H, Bharathy G, Prasad M (2022) Exploiting linguistic information from Nepali transcripts for early detection of Alzheimer\u2019s disease using natural language processing and machine learning techniques. Int J Hum\u2013Comput Stud 160:102761","journal-title":"Int J Hum\u2013Comput Stud"},{"key":"1167_CR3","unstructured":"Aduragba OT, Yu J, Cristea AI, Shi L (2021) Detecting fine-grained emotions on social media during major disease outbreaks: health and well-being before and during the Covid-19 pandemic. In: AMIA annual symposium proceedings, vol 2021. American Medical Informatics Association, p 187"},{"key":"1167_CR4","doi-asserted-by":"crossref","unstructured":"Aguilera J, Far\u00edas DIH, Montes-y-G\u00f3mez M, Gonz\u00e1lez LC (2021) Detecting traces of self-harm in social media: a simple and interpretable approach. In: Mexican international conference on artificial intelligence. Springer, Berlin, pp 196\u2013207","DOI":"10.1007\/978-3-030-89820-5_16"},{"key":"1167_CR5","doi-asserted-by":"crossref","unstructured":"Arag\u00f3n ME, L\u00f3pez-Monroy AP, Montes-y-G\u00f3mez M (2019) Inaoe-cimat at erisk 2019: detecting signs of anorexia using fine-grained emotions. In: CLEF (working notes)","DOI":"10.18653\/v1\/N19-1151"},{"key":"1167_CR6","unstructured":"Barros L, Trifan A, Oliveira JL (2021) Vader meets BERT: sentiment analysis for early detection of signs of self-harm through social mining. In: CLEF (working notes), pp 897\u2013907"},{"key":"1167_CR7","doi-asserted-by":"crossref","unstructured":"Baytas IM, Xiao C, Zhang X, Wang F, Jain AK, Zhou J (2017) Patient subtyping via time-aware lstm networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 65\u201374","DOI":"10.1145\/3097983.3097997"},{"key":"1167_CR8","unstructured":"Beltagy I, Peters ME, Cohan A (2020) Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150"},{"issue":"2","key":"1167_CR9","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1111\/papt.12222","volume":"92","author":"S Bucci","year":"2019","unstructured":"Bucci S, Schwannauer M, Berry N (2019) The digital revolution and its impact on mental health care. Psychol Psychother Theory Res Pract 92(2):277\u2013297","journal-title":"Psychol Psychother Theory Res Pract"},{"key":"1167_CR10","doi-asserted-by":"crossref","unstructured":"Burke M, Marlow C, Lento T (2010) Social network activity and social well-being. In: Proceedings of the SIGCHI conference on human factors in computing systems, pp 1909\u20131912","DOI":"10.1145\/1753326.1753613"},{"key":"1167_CR11","unstructured":"Campillo-Ageitos E, Fabregat H, Araujo L, Martinez-Romo J (2021) Nlp-uned at erisk 2021: self-harm early risk detection with tf-idf and linguistic features. In: Working notes of CLEF, pp 21\u201324"},{"key":"1167_CR12","doi-asserted-by":"crossref","unstructured":"Cao L, Zhang H, Feng L, Wei Z, Wang X, Li N, He X (2019) Latent suicide risk detection on microblog via suicide-oriented word embeddings and layered attention. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 1718\u20131728","DOI":"10.18653\/v1\/D19-1181"},{"key":"1167_CR13","doi-asserted-by":"crossref","unstructured":"Chen Z, Ma Q, Lin Z (2021) Time-aware multi-scale rnns for time series modeling. In: IJCAI, pp 2285\u20132291","DOI":"10.24963\/ijcai.2021\/315"},{"key":"1167_CR14","doi-asserted-by":"crossref","unstructured":"Cho K, Van\u00a0Merri\u00ebnboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder\u2013decoder approaches. arXiv preprint arXiv:1409.1259","DOI":"10.3115\/v1\/W14-4012"},{"key":"1167_CR15","doi-asserted-by":"crossref","unstructured":"Cui Y, Jia M, Lin T-Y, Song Y, Belongie S (2019) Class-balanced loss based on effective number of samples. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 9268\u20139277","DOI":"10.1109\/CVPR.2019.00949"},{"key":"1167_CR16","doi-asserted-by":"crossref","unstructured":"De\u00a0Choudhury M, Gamon M, Counts S, Horvitz E (2013) Predicting depression via social media. In: Proceedings of the international AAAI conference on web and social media, vol 7","DOI":"10.1609\/icwsm.v7i1.14432"},{"key":"1167_CR17","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M-W, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the Association for Computational Linguistics: human language technologies, Volume 1 (long and short papers), pp 4171\u20134186. Association for Computational Linguistics, Minneapolis, MN. https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"1167_CR18","doi-asserted-by":"crossref","unstructured":"Gaur M, Alambo A, Sain JP, Kursuncu U, Thirunarayan K, Kavuluru R, Sheth A, Welton R, Pathak J (2019) Knowledge-aware assessment of severity of suicide risk for early intervention. In: The World Wide Web conference, pp 514\u2013525","DOI":"10.1145\/3308558.3313698"},{"issue":"8","key":"1167_CR19","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"1167_CR20","unstructured":"Kang X, Dou R, Yu H (2022) Tua1 at erisk 2022: exploring affective memories for early detection of depression"},{"key":"1167_CR21","doi-asserted-by":"crossref","unstructured":"Keymanesh M, Gurukar S, Boettner B, Browning C, Calder C, Parthasarathy S (2020) Twitter watch: leveraging social media to monitor and predict collective-efficacy of neighborhoods. In: Complex networks XI: proceedings of the 11th conference on complex networks CompleNet 2020. Springer, Berlin, pp 197\u2013211","DOI":"10.1007\/978-3-030-40943-2_17"},{"key":"1167_CR22","doi-asserted-by":"crossref","unstructured":"Lin T-Y, Goyal P, Girshick R, He K, Doll\u00e1r P (2017) Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision, pp 2980\u20132988","DOI":"10.1109\/ICCV.2017.324"},{"key":"1167_CR23","doi-asserted-by":"crossref","unstructured":"Maneriker P, He Y, Parthasarathy S (2021) Sysml: stylometry with structure and multitask learning: implications for darknet forum migrant analysis. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 6844\u20136857","DOI":"10.18653\/v1\/2021.emnlp-main.548"},{"key":"1167_CR24","first-page":"15574","volume":"35","author":"A Manolache","year":"2022","unstructured":"Manolache A, Brad F, Barbalau A, Ionescu RT, Popescu M (2022) Veridark: a large-scale benchmark for authorship verification on the dark web. Adv Neural Inf Process Syst 35:15574\u201315588","journal-title":"Adv Neural Inf Process Syst"},{"key":"1167_CR25","unstructured":"Mart\u00ednez-Casta\u00f1o R, Htait A, Azzopardi L, Moshfeghi Y (2020) Early risk detection of self-harm and depression severity using BERT-based transformers: ilab at clef erisk 2020"},{"key":"1167_CR26","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Casta\u00f1o R, Htait A, Azzopardi L, Moshfeghi Y (2021) BERT-based transformers for early detection of mental health illnesses. In: International conference of the cross-language evaluation forum for european languages. Springer, pp 189\u2013200","DOI":"10.1007\/978-3-030-85251-1_15"},{"key":"1167_CR27","doi-asserted-by":"crossref","unstructured":"Matero M, Idnani A, Son Y, Giorgi S, Vu H, Zamani M, Limbachiya P, Guntuku SC, Schwartz HA (2019) Suicide risk assessment with multi-level dual-context language and BERT. In: Proceedings of the sixth workshop on computational linguistics and clinical psychology, pp 39\u201344","DOI":"10.18653\/v1\/W19-3005"},{"key":"1167_CR28","unstructured":"Maupom\u00e9 D, Armstrong MD, Rancourt F, Soulas T, Meurs M-J (2021) Early detection of signs of pathological gambling, self-harm and depression through topic extraction and neural networks. In: CLEF (working notes), pp 1031\u20131045"},{"key":"1167_CR29","doi-asserted-by":"crossref","unstructured":"Naseem U, Dunn AG, Kim J, Khushi M (2022a) Early identification of depression severity levels on reddit using ordinal classification. In: Proceedings of the ACM web conference 2022, pp 2563\u20132572","DOI":"10.1145\/3485447.3512128"},{"key":"1167_CR30","first-page":"66","volume":"6","author":"U Naseem","year":"2022","unstructured":"Naseem U, Khushi M, Kim J, Dunn AG (2022) Hybrid text representation for explainable suicide risk identification on social media. IEEE Trans Comput Soc Syst 6:66","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"1167_CR31","doi-asserted-by":"crossref","unstructured":"Naseem U, Kim J, Khushi M, Dunn AG (2022c) Identification of disease or symptom terms in reddit to improve health mention classification. In: Proceedings of the ACM web conference 2022, pp 2573\u20132581","DOI":"10.1145\/3485447.3512129"},{"key":"1167_CR32","doi-asserted-by":"crossref","unstructured":"Naseem U, Lee BC, Khushi M, Kim J, Dunn AG (2022d) Benchmarking for public health surveillance tasks on social media with a domain-specific pretrained language model. arXiv preprint arXiv:2204.04521","DOI":"10.18653\/v1\/2022.nlppower-1.3"},{"key":"1167_CR33","doi-asserted-by":"crossref","unstructured":"Naseem U, Kim J, Khushi M, Dunn A (2023) Graph-based hierarchical attention network for suicide risk detection on social media. In: Companion proceedings of the ACM web conference 2023, pp 995\u20131003","DOI":"10.1145\/3543873.3587587"},{"key":"1167_CR34","unstructured":"Nasim M, Weber D, South T, Tuke J, Bean N, Falzon L, Mitchell L (2022) Are we always in strife? A longitudinal study of the echo chamber effect in the Australian twittersphere. arXiv preprint arXiv:2201.09161"},{"key":"1167_CR35","unstructured":"Organization WH et al (2022) World mental health report: transforming mental health for all"},{"key":"1167_CR36","unstructured":"Ragheb W, Az\u00e9 J, Bringay S, Servajean M (2019) Attentive multi-stage learning for early risk detection of signs of anorexia and self-harm on social media. In: CLEF 2019 working notes-conference and labs of the evaluation forum, vol 2380, p 126"},{"issue":"2","key":"1167_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3437259","volume":"2","author":"EA R\u00edssola","year":"2021","unstructured":"R\u00edssola EA, Losada DE, Crestani F (2021) A survey of computational methods for online mental state assessment on social media. ACM Trans Comput Healthc 2(2):1\u201331","journal-title":"ACM Trans Comput Healthc"},{"key":"1167_CR38","doi-asserted-by":"crossref","unstructured":"Sawhney R, Manchanda P, Mathur P, Shah R, Singh R (2018a) 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","DOI":"10.18653\/v1\/W18-6223"},{"key":"1167_CR39","doi-asserted-by":"crossref","unstructured":"Sawhney R, Manchanda P, Singh R, Aggarwal S (2018b) A computational approach to feature extraction for identification of suicidal ideation in tweets. In: Proceedings of ACL 2018, student research workshop, pp 91\u201398","DOI":"10.18653\/v1\/P18-3013"},{"key":"1167_CR40","doi-asserted-by":"crossref","unstructured":"Sawhney R, Joshi H, Gandhi S, Shah R (2020) A time-aware transformer based model for suicide ideation detection on social media. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 7685\u20137697","DOI":"10.18653\/v1\/2020.emnlp-main.619"},{"key":"1167_CR41","doi-asserted-by":"crossref","unstructured":"Sawhney R, Agarwal S, Neerkaje AT, Aletras N, Nakov P, Flek L (2022) Towards suicide ideation detection through online conversational context. In: Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval, pp 1716\u20131727","DOI":"10.1145\/3477495.3532068"},{"key":"1167_CR42","doi-asserted-by":"crossref","unstructured":"Thapa S, Adhikari S, Naseem U, Singh P, Bharathy G, Prasad M (2020) Detecting Alzheimer\u2019s disease by exploiting linguistic information from Nepali transcript. In: Neural information processing: 27th international conference, ICONIP 2020, Bangkok, Thailand, November 18\u201322, 2020, Proceedings, Part IV 27. Springer, Berlin, pp 176\u2013184","DOI":"10.1007\/978-3-030-63820-7_20"},{"key":"1167_CR43","doi-asserted-by":"crossref","unstructured":"Thapa S, Ghimire A, Adhikari S, Bhoi AK, Barsocchi P (2022) Cognitive internet of things (iot) and computational intelligence for mental well-being. In: Cognitive and soft computing techniques for the analysis of healthcare data. Elsevier, Berlin, pp 59\u201377","DOI":"10.1016\/B978-0-323-85751-2.00004-9"},{"key":"1167_CR44","doi-asserted-by":"crossref","unstructured":"Tsakalidis A, Nanni F, Hills A, Chim J, Song J, Liakata M (2022) Identifying moments of change from longitudinal user text. arXiv preprint arXiv:2205.05593","DOI":"10.18653\/v1\/2022.acl-long.318"},{"key":"1167_CR45","unstructured":"Un\u00a0Nisa Q, Muhammad R (2021) Towards transfer learning using BERT for early detection of self-harm of social media users. In: Proceedings of the working notes of CLEF, pp 21\u201324"},{"key":"1167_CR46","doi-asserted-by":"crossref","unstructured":"Wang X, Brown DE, Gerber MS (2012) Spatio-temporal modeling of criminal incidents using geographic, demographic, and twitter-derived information. In: 2012 IEEE international conference on intelligence and security informatics. IEEE, pp 36\u201341","DOI":"10.1109\/ISI.2012.6284088"},{"key":"1167_CR47","doi-asserted-by":"crossref","unstructured":"Weber D, Nasim M, Mitchell L, Falzon L (2020) A method to evaluate the reliability of social media data for social network analysis. In: 2020 IEEE\/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 317\u2013321","DOI":"10.1109\/ASONAM49781.2020.9381461"},{"issue":"2","key":"1167_CR48","first-page":"320","volume":"57","author":"ML Williams","year":"2017","unstructured":"Williams ML, Burnap P, Sloan L (2017) Crime sensing with big data: the affordances and limitations of using open-source communications to estimate crime patterns. Brit J Criminol 57(2):320\u2013340","journal-title":"Brit J Criminol"},{"key":"1167_CR49","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1016\/j.inffus.2022.11.031","volume":"92","author":"T Zhang","year":"2023","unstructured":"Zhang T, Yang K, Ji S, Ananiadou S (2023) Emotion fusion for mental illness detection from social media: a survey. Inf Fusion 92:231\u2013246","journal-title":"Inf Fusion"},{"key":"1167_CR50","unstructured":"Zhou X, Coiera E, Tsafnat G, Arachi D, Ong M-S, Dunn AG et al (2015) Using social connection information to improve opinion mining: identifying negative sentiment about hpv vaccines on Twitter"},{"key":"1167_CR51","unstructured":"Zogan H, Razzak I, Jameel S, Xu G (2021) Depressionnet: a novel summarization boosted deep framework for depression detection on social media. arXiv preprint arXiv:2105.10878"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-023-01167-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-023-01167-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-023-01167-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T05:35:17Z","timestamp":1740461717000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-023-01167-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,10]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1167"],"URL":"https:\/\/doi.org\/10.1007\/s13278-023-01167-9","relation":{},"ISSN":["1869-5469"],"issn-type":[{"value":"1869-5469","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,10]]},"assertion":[{"value":"28 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"9"}}