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Social media has emerged over the last decade and consequently has accompanying information systems (IS) research. In this paper, however, we argue that researchers cannot keep pace with the rapid changes this topic engenders. This claim is substantiated by a database built from 28,283 articles published in either top IS journals, i.e., ELMA, BISE, and all AIS basket journals, or IS conference proceedings, i.e., ICIS, ECIS, AMCIS, WI, HICSS, and PACIS. We derive a set of 13 keywords that have been used to describe what we call the ‘social media phenomenon’. The results are transformed into a four-phase model and a taxonomical categorization, which provides an exhaustive overview of social media terms and trends and reflects the main research issue facing academia: The fact that the versatile social media phenomenon in its current phase is dominated by just two terms—“social media” and “social network”—or imprecise “such-as-Facebook” analogies.

Triggered by popular cases such as political election campaigns in the United States of America and the United Kingdom, research on fake news, particularly in the context of social media, has gained growing importance recently. Our chapter deals with the individual user’s perspective and places the focus on the competency to detect fake news – the so-called fake news detection literacy. One main challenge in this field is the empirical measurement of such an individual fake news detection literacy. Based on our previous research, we suggest an extended version of a general social media information literacy (SMIL) model which is enriched with respect to the context of fake news, i.e., mainly the evaluation of information. The extended model is empirically tested by applying correlation analyses based on a sample of n = 96. The updated construct provides a way to measure fake news detection literacy and offers various avenues for further research that are discussed at the end of the chapter.

Part 6: Social Media and Analytics; International audience; In times of increasing importance of social media services, we have to rethink information literacy. One of the key assumptions of existing information literacy constructs is “static” information, meaning that information does not change. But compared to traditional and mostly unidirectional media services such as printed newspapers or television, this does not reflect the reality of a social media context. Here, information can be characterized as “dynamic”, meaning that, for example, every user can easily modify information before sharing it (again). A measurement construct covering these novel aspects of information literacy is missing, so far. Thus, the main objective of this paper is to develop a rigor and updated construct to measure and quantify social media information literacy of an individual social media user. We selected a comprehensive construct development framework that guided us through the investigation, and which includes qualitative as well as quantitative analyses. The outcome is a theoretically grounded and empirically derived social media information literacy (SMIL) construct. The paper ends with a discussion of potential future research directions.

Electronic word-of-mouth (eWOM) has attracted a great deal of attention in both academia and practice in recent years. Our empirical study investigates the relation of negative comments and consumers’ trust in fashion presentations focusing on clothing. It is a product category characterized by a lot of online conversation and opinion sharing, but is surprisingly under-researched regarding eWOM. We calculate a multiple regression model moderated by consumers’ social media experience to identify the impact of a dichotomous eWOM stimulus. We find that negative comments referring to a fashion presentation cause a significant decrease of trust. However, the more experienced a user is in dealing with social media services such as Facebook or YouTube, the weaker this effect will get. These findings contribute to the existing academic discourse about the impact of negative eWOM.

Part 3: Big and Open Data; International audience; Facebook, Twitter, Instagram, and other players in the social media world have been on the rise during the last couple of years. In contrast to their popularity, their underlying business models are vague and often only linked to advertising. In this explorative study we identify new revenue sources for social media service providers besides advertising. Based on three use cases with Facebook, Tencent, and LinkedIn, we identify three possibly fruitful ways to extend existing social media business models. Subsequently, a survey with 301 respondents changes perspectives on the user’s willingness to pay in order to identify usage-related differences evoked by cultural and external circumstances. Four derived hypotheses lead the way to avenues of further research especially in terms of Big Data analytics with new e-commerce trends like Facebook’s Buy Button.