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In March 2018, news of the Facebook-Cambridge Analytica scandal made headlines around the world. By inappropriately collecting data from approximately 87 million users' Facebook profiles, the data analytics company, Cambridge Analytica, created psychographically tailored advertisements that allegedly aimed to influence people's voting preferences in the 2016 US presidential election. In the aftermath of this incident, we conducted a series of semi-structured interviews with 30 participants based at a UK university, discussing their understanding of online privacy and how they manage it in the wake of the scandal. We analysed this data using an inductive (i.e. 'bottom-up') thematic analysis approach. Contrary to many opinions reported in the news, the respondents in our sample did not delete their accounts, frantically change their privacy settings, or even express that much concern. As a result, individuals often consider themselves immune to psychographically tailored advertisements, and lack understanding of how automated approaches and algorithms work in relation to their (and their networks') personal data. We discuss our findings in relation to wider related research (e.g. crisis fatigue, networked privacy, Protection Motivation Theory) and discuss directions for future research.

Is it possible to judge someone accurately from his or her online activity? The Internet provides vast opportunities for individuals to present themselves in different ways, from simple self-enhancement to malicious identity fraud. We often rely on our Internet-based judgments of others to make decisions, such as whom to socialize with, date, or employ. Recently, personality-perception researchers have turned to studying social media and digital devices in order to ask whether a person's digital traces can reveal aspects of his or her identity. Simultaneously, advances in "big data" analytics have demonstrated that computer algorithms can predict individuals' traits from their digital traces. In this article, we address three questions: What do we currently know about human-and computer-based personality assessments? How accurate are these assessments? Where are these fields heading? We discuss trends in the current findings, provide an overview of methodological approaches, and recommend directions for future research.

To what extent does our online activity reveal who we are? Recent research has demonstrated that the digital traces left by individuals as they browse and interact with others online may reveal who they are and what their interests may be. In the present paper we report a systematic review that synthesises current evidence on predicting demographic attributes from online digital traces. Studies were included if they met the following criteria: (i) they reported findings where at least one demographic attribute was predicted/inferred from at least one form of digital footprint, (ii) the method of prediction was automated, and (iii) the traces were either visible (e.g. tweets) or non-visible (e.g. clickstreams). We identified 327 studies published up until October 2018. Across these articles, 14 demographic attributes were successfully inferred from digital traces; the most studied included gender, age, location, and political orientation. For each of the demographic attributes identified, we provide a database containing the platforms and digital traces examined, sample sizes, accuracy measures and the classification methods applied. Finally, we discuss the main research trends/findings, methodological approaches and recommend directions for future research.

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