Technological developments have changed information and communication in society by extending the possibilities to collect, process, and share internet user data to optimize communication. This widespread data collection results in companies being one of the main sources of surveillance. The current study explores internet user experiences and coping with corporate surveillance on the cognitive, affective, and behavioral level. Semi-structured interviews were conducted with 22 U.S. adults. The results show that corporate surveillance is mostly understood as the data collection by corporations/advertisers online. Online behavioral advertising is the most commonly given example of an instance of surveillance, followed by devices listening (i.e., conversation-related advertising). Regarding affective coping, we observed a ‘surveillance paradox’ as internet users are positive, negative or indifferent about corporate surveillance. Internet users employ different privacy protection measures as well as chilling effects in the form of limiting media ownership, minimizing use, information search, and information sharing. The latter is an ethical consequence of corporate surveillance and might have negative consequences for the autonomy of internet users and subsequently impact the companies that rely on internet user data.

TikTok has been criticized for its low privacy standards, but little is known about how its adolescent users protect their privacy. Based on interviews with 54 adolescents in Switzerland, this study provides a comprehensive understanding of young TikTok users' privacy management practices related to the creation of videos. The data were explored using the COM-B model, an established behavioral analysis framework adapted for sociotechnical privacy research. Our overall findings are in line with previous research on other social networks: adolescents are aware of privacy related to their online social connections (social privacy) and perform conscious privacy management. However, we also identified new patterns related to the central role of algorithmic recommendations potentially relevant for other social networks. Adolescents are aware that TikTok's special algorithm, combined with the app's high prevalence among their peers, could easily put them in the spotlight. Some adolescents also reduce TikTok, which was originally conceived as a social network, to its extensive audio-visual capabilities and share TikToks via more private channels (e.g., Snapchat) to manage audiences and avoid identification by peers. Young users also find other creative ways to protect their privacy such as identifying stalkers or maintaining multiple user accounts with different privacy settings to establish granular audience management. Based on our findings, we propose various concrete measures to develop interventions that protect the privacy of adolescents on TikTok.

The COVID-19 pandemic has created one of the largest medical, financial, and social disruption in history. In the fight against this virus, many European governments have turned to collecting and using online data (for various technological applications) as a key strategic remedy. This study consists of data from a national representative survey in the Netherlands focusing on the extent to which data-driven technologies from the government can count on the support of the general public. By focusing on trust perceptions, risk beliefs and privacy concerns, we introduce a typology consisting of three subgroups: the sceptical, the carefree, and the neutral respondents. It was found that each of the three groups exhibit unique demographic characteristics. In addition, findings also revealed that these three identified groups have different support levels for specific digital solutions from the government. These findings contribute to an important and timely debate and entail relevant policy implications with regard to the democratic legitimation of data-driven technologies in times of COVID-19.

We present INCA (short for INfrastructure for Content Analysis), a Python module for collecting, storing, processing, and analyzing a wide variety of media content, including but not limited to news, political debates, social media, forums, and customer reviews. Using Elasticsearch as a database backend and Celery for task management, it makes automated content analysis scalable. INCA's main objective is to enable and promote an integrated workflow. INCA focuses on re-usability of data, processors, and analyses; making all steps of automated content analysis (ACA) accessible to social scientists, without requiring advanced programming skills. Here, we present the aim, implementation and recommended workflow for INCA.