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PurposeThis 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.Design/methodology/approachParticipants 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.FindingsFacebook 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.Originality/valueDigital 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.

Information asymmetry between sellers and buyers is inherent in online markets where transactions often occur between strangers. Trust-building mechanisms such as seller feedback ratings have reduced these problems because a seller’s feedback ratings build buyers’ trust in the seller before they engage in a transaction. However, these ratings are retrospective, that is, they generate information about a transaction after it is completed, rather than during the transaction itself. Additionally, they are based on other users’ experiences, possibly in different contexts, not based on any direct interaction between the prospective buyer and the seller. To address this problem, we study public buyer–seller engagement via question and answer during online auctions and find that seller engagement (responding to buyers’ questions) can affect buyer behavior, including those who do not ask any questions. Our analysis shows that the impact of the seller’s engagement on buyer behavior varies with product type and seller reputation (feedback ratings). A key insight is that sellers with higher reputation reap greater benefits from this engagement than other sellers. We also find that the cost of an additional negative feedback rating outweighs the benefit of a positive one.