Investigating the value of privacy on online social networks: conjoint analysis

Popularity of Online Social Networks has been recently overshadowed by the privacy problems they pose. Users are getting increasingly vigilant concerning information they disclose and are strongly opposing the use of their information for commercial purposes. Nevertheless, as long as the network is offered to users for free, providers have little choice but to generate revenue through personalized advertising to remain financially viable. Our study empirically investigates the ways out of this deadlock. Using conjoint analysis we find that privacy is indeed important for users. We identify three groups of users with different utility patterns: Unconcerned Socializers, Control-conscious Socializers and Privacy-concerned. Our results provide relevant insights into how network providers can capitalize on different user preferences by specifically addressing the needs of distinct groups in the form of various premium accounts. Overall, our study is the first attempt to assess the value of privacy in monetary terms in this context.

Multimodal viral communication: a social semiotic analysis of the popular Spanish "Ola ke ase" meme

Multimodality – the interdependence of semiotic resources in text – is an existential element of today’s media. The term multimodality attends systematically to the social interpretation of a wide range of communicational forms used in meaning making. A primary focus of social- semiotic multimodal analysis is on mapping how modal resources are used by people in a given social context. In November 2012 the “Ola ke ase” catchphrase, which is a play on “Hola ¿qué hace?”, appeared for the first time in Spain and immediately has been adopted as a Twitter hashtag and an image macro series. Its viral spread on social networks has been tremendous, being a trending topic in various Spanish-speaking countries. The objective of analysis is how language and image work together in the “Ola ke ase” meme. The interplay between text and image in one of the original memes and some of its variations is quantitatively analysed applying a social-semiotic approach. Results demonstrate how the “Ola ke ase” meme functions through its multimodal character and the non-standard orthography. The spread of uncountable variations of the meme shows the social process that goes on in the meaning making of the semiotic elements.

Social networks, mobility, and exchange systems during the Neolithic and Bronze Age of the Swiss Plateau

Excavations of Neolithic (4000 – 3500 BC) and Late Bronze Age (1200 – 800 BC) wetland sites on the northern Alpine periphery have produced astonishing and detailed information about the life and human environment of prehistoric societies. It is even possible to reconstruct settlement histories and settlement dynamics, which suggest a high degree of mobility during the Neolithic. Archaeological finds—such as pottery—show local typological developments in addition to foreign influences. Furthermore, exogenous lithic forms indicate far reaching interaction. Many hundreds of bronze artefacts are recorded from the Late Bronze Age settlements, demonstrating that some wetland sites were centres of bronzework production. Exogenous forms of bronzework are relatively rare in the wetland settlements during the Late Bronze Age. However, the products produced in the lake-settlements can be found widely across central Europe, indicating their continued involvement in interregional exchange partnerships. Potential motivations and dynamics of the relationships between sites and other regions of Europe will be detailed using case studies focussing on the settlements Seedorf Lobsigensee (BE), Concise (VD), and Sutz-Lattrigen Hauptstation innen (BE), and an initial assessment of intra-site connectivity through Network Analysis of sites within the region of Lake Neuchâtel, Lake Biel, and Lake Murten.

Social Network Analysis and Qualitative Comparative Analysis: Their mutual benefit for the explanation of policy network structures

By switching the level of analysis and aggregating data from the micro-level of individual cases to the macro-level, quantitative data can be analysed within a more case-based approach. This paper presents such an approach in two steps: In a first step, it discusses the combination of Social Network Analysis (SNA) and Qualitative Comparative Analysis (QCA) in a sequential mixed-methods research design. In such a design, quantitative social network data on individual cases and their relations at the micro-level are used to describe the structure of the network that these cases constitute at the macro-level. Different network structures can then be compared by QCA. This strategy allows adding an element of potential causal explanation to SNA, while SNA-indicators allow for a systematic description of the cases to be compared by QCA. Because mixing methods can be a promising, but also a risky endeavour, the methodological part also discusses the possibility that underlying assumptions of both methods could clash. In a second step, the research design presented beforehand is applied to an empirical study of policy network structures in Swiss politics. Through a comparison of 11 policy networks, causal paths that lead to a conflictual or consensual policy network structure are identified and discussed. The analysis reveals that different theoretical factors matter and that multiple conjunctural causation is at work. Based on both the methodological discussion and the empirical application, it appears that a combination of SNA and QCA can represent a helpful methodological design for social science research and a possibility of using quantitative data with a more case-based approach.

Online social networks: why we disclose

On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of industry players and policymakers worldwide. Despite the impressive scope of this phenomenon, very little is understood about what motivates users to disclose personal information. Integrating focus group results into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

Privacy concerns and identity in online social networks

Driven by privacy-related fears, users of Online Social Networks may start to reduce their network activities. This trend can have a negative impact on network sustainability and its business value. Nevertheless, very little is understood about the privacy-related concerns of users and the impact of those concerns on identity performance. To close this gap, we take a systematic view of user privacy concerns on such platforms. Based on insights from focus groups and an empirical study with 210 subjects, we find that (i) Organizational Threats and (ii) Social Threats stemming from the user environment constitute two underlying dimensions of the construct “Privacy Concerns in Online Social Networks”. Using a Structural Equation Model, we examine the impact of the identified dimensions of concern on the Amount, Honesty, and Conscious Control of individual self-disclosure on these sites. We find that users tend to reduce the Amount of information disclosed as a response to their concerns regarding Organizational Threats. Additionally, users become more conscious about the information they reveal as a result of Social Threats. Network providers may want to develop specific mechanisms to alleviate identified user concerns and thereby ensure network sustainability.

Leveraging Trust and Privacy Concerns in Online Social Networks: an Empirical Study

Unprecedented success of Online Social Networks, such as Facebook, has been recently overshadowed by the privacy risks they imply. Weary of privacy concerns and unable to construct their identity in the desired way, users may restrict or even terminate their platform activities. Even though this means a considerable business risk for these platforms, so far there have been no studies on how to enable social network providers to address these problems. This study fills this gap by adopting a fairness perspective to analyze related measures at the disposal of the provider. In a Structural Equation Model with 237 subjects we find that ensuring interactional and procedural justice are two important strategies to support user participation on the platform.