Identifying Medical Self-Disclosure in Online Communities

Abstract

Self-disclosure in online health conversations may offer a host of benefits, including earlier detection and treatment of medical issues that may have otherwise gone unaddressed. However, research analyzing medical selfdisclosure in online communities is limited. We address this shortcoming by introducing a new dataset of health-related posts collected from online social platforms, categorized into three groups (NO SELF-DISCLOSURE, POSSI-BLE SELF-DISCLOSURE, and CLEAR SELF-DISCLOSURE) with high inter-annotator agreement (κ = 0.88). We make this data available to the research community. We also release a predictive model trained on this dataset that achieves an accuracy of 81.02%, establishing a strong performance benchmark for this task.

Characterizing and Identifying Socially Shared Self-Descriptions in Product Reviews

“…Previous research found that high self-disclosure social media posts receive fewer upvotes, but more comments and a higher response rate (Balani and De Choudhury 2015;Valizadeh et al 2021). Researchers observed a negative association between self-disclosure and network size on Twitter (Wang, Burke, and Kraut 2016).…”

“…We labeled these reviews based on broad categories derived from different types of self-disclosure described in prior work. These categories consisted mostly of self disclosure in areas other than e-commerce, such as search (Weber and Jaimes 2011;Bi et al 2013), online profiles (in e-commerce and social media) (Ma, Hancock, and Naaman 2016;Ma et al 2017), social media platforms (Bak, Kim, and Oh 2012;Wang, Burke, and Kraut 2016;Zhao et al 2016;Walton and Rice 2013;Saha et al 2021;Zhao et al 2014Zhao et al , 2016 and online communities (Kou and Gray 2018;Valizadeh et al 2021;Balani and De Choudhury 2015;Yang, Yao, and Kraut 2017;Barak and Gluck-Ofri 2007;Yang, Yao, and Kraut 2017).…”

Year 2021: COVID-19, Information Extraction and BERTization among the Hottest Topics in Medical Natural Language Processing

“…• detection of events [29,30], including self-harm events [31]; • extraction of diagnoses [13,[32][33][34][35] and their codes [36][37][38]; • recognition of named entities [5,14,[39][40][41], and more specifically of personal information [21,42,43] and family history [20]; • localization of advices [44] and arguments [45] in scientific literature; • extraction of relations [46][47][48], including temporal [49] and causality [50,51] relations.…”

Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses

“…Predicting depression on social media is a long standing research track (De Choudhury et al, 2021), and social media has also shown that signals to identify suicidal ideation can be traced with high efficacy (Choudhury et al, 2016). Platforms like Reddit 4 can be instrumental in terms of support, resources, and self-disclosure about mental health (Choudhury and De, 2014;Valizadeh et al, 2021). One of the first traceable thematic identifications of correlated, quantifiable information regarding mental state and wellbeing was by Fleming et al (1992), suggesting that a lack of social support combined with social isolation was present in patients showing signs of depression or post-partum depression.…”