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This study investigates associations between Facebook (FB) conversations and self-reports of substance use among youth experiencing homelessness (YEH). YEH engage in high rates of substance use and are often difficult to reach, for both research and interventions. Social media sites provide rich digital trace data for observing the social context of YEH's health behaviors. The authors aim to investigate the feasibility of using these big data and text mining techniques as a supplement to self-report surveys in detecting and understanding YEH attitudes and engagement in substance use.

Participants took a self-report survey in addition to providing consent for researchers to download their Facebook feed data retrospectively. The authors collected survey responses from 92 participants and retrieved 33,204 textual Facebook conversations. The authors performed text mining analysis and statistical analysis including ANOVA and logistic regression to examine the relationship between YEH's Facebook conversations and their substance use.

Facebook posts of YEH have a moderately positive sentiment. YEH substance users and non-users differed in their Facebook posts regarding: (1) overall sentiment and (2) topics discussed. Logistic regressions show that more positive sentiment in a respondent's FB conversation suggests a lower likelihood of marijuana usage. On the other hand, discussing money-related topics in the conversation increases YEH's likelihood of marijuana use.

Digital trace data on social media sites represent a vast source of ecological data. This study demonstrates the feasibility of using such data from a hard-to-reach population to gain unique insights into YEH's health behaviors. The authors provide a text-mining-based toolkit for analyzing social media data for interpretation by experts from a variety of domains.

Social media is becoming a hub of fake content, be it political news, product reviews, business promotion or any other sociocultural event. This study aims to provide a comprehensive review of the emerging literature to advance an understanding of misinformation on social media platforms, which is a growing concern these days.

The authors curate and synthesize the dispersed knowledge about misinformation on social media by conducting a systematic literature review based on the preferred reporting items for systematic reviews and meta-analyses framework. The search strategy resulted in 446 research articles, out of which 33 relevant articles were identified for this research.

Misinformation on social media spreads swiftly and may result in negative consequences. This review identifies 13 intrinsic predictors of the dissemination, 11 detection approaches and 10 ways to combat misinformation on social media.

The study adds to the present knowledge of spread and detection of misinformation on social media. The results of this study will be beneficial for researchers and practitioners and help them in mitigating the harmful consequences of the spread of misinformation.