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Social implications of social mediaThe rapid adoption of social networking sites (SNSs) raises important questions about the social implications of such usage. Drawing on unique longitudinal data, surveying a representative sample of Norwegian online users (N = 2,000, in 3 annual waves (2008, 2009, and 2010) IntroductionThere is currently a strong debate on the social impact of computer-mediated communication (CMC) in general and the usage of social networking sites (SNSs) in particular. An important reason for this debate is that there are no studies that compare nonusers with SNS users. Longitudinal research that covers a broader part of the population is also lacking in this field. In the absence of research findings to report on, the media is forced to report on anecdotal evidence, which fuels ''antisocial networking'' claims about the negative social implications of SNSs (e.g., The New York Times, 2010, April 30). The heavy social cost of using SNSs is also assumed by some academics to be decreased social involvement, less face-to-face interaction, and increased loneliness or isolation (e.g., Morris, 2010;Turkle, 2011). The relevance of this problem is illustrated by Turkle's (2011) newest book Alone Together, which concludes that our social preferences are evolving to include, and in some cases favor, technology over people. As stated by Kraut et al. (1998Kraut et al. ( , p. 1017 467that often use only two possible outcomes (e.g., heavy use or low use). By simply looking at one SNS at a time, we might miss the important fact that SNS usage involves systematic patterns in which the same SNS can be used for different purposes, and different SNSs can be used for the same purpose (Brandtzaeg & Heim, 2011). These limitations leave four basic questions unanswered:1 How SNS users compare to nonusers in terms of their social capital 2 How to achieve a more precise identification of various usage patterns by means of different user types of SNSs in general, as opposed to a random or continuous distribution of SNS usage across users in a specific SNS 3 Whether distinct SNS user types relates to different outcomes of offline social capital 4 Whether SNS usage in general and distinct SNS user types in particular show variation over time in their offline social capitalThis study addresses these limitations by using a unique and extensive set of longitudinal data from a representative sample of online users (N = 2,000, age 15-75 years) in Norway from 2008 to 2010. Norway has an ideal population for studying the social impact of SNSs, given its exceptionally high Internet and SNSs connectivity (Internet World Statistics, 2010). The study compared both the level of social capital among SNS users and nonusers, and the level of social capital among distinct SNS user types. The results will, hopefully, serve as an important contribution to more complete knowledge on how SNS usage in general as well as particular uses of SNSs relates to the formation of social capital over time.

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A potential revolution is happening in front of our eyes. For decades, researchers and practitioners in human-computer interaction (HCI) have been improving their skills in designing for graphical user interfaces. Now things may take an unexpected turn-toward natural-language user interfaces, in which interaction with digital systems happens not through scrolling, swiping, or button clicks, but rather through strings of text in natural language. This is particularly visible in recent developments in chatbots, that is, machine agents serving as natural-language user interfaces to data and service providers [1], typically in the context of messaging applications. Need a reminder to pick up some flowers for your husband on the way home? Ask Jarvis the chatbot to remind you. Wonder if you should bring an umbrella to that meeting in Stockholm? Send Poncho the artificial weather cat a message and ask. If technology giants like Google, Facebook, and Microsoft are right, we will be moving our digital interaction from websites and apps with graphical user interfaces to messaging platforms such as Messenger and Allo. If this happens, huge challenges and opportunities await in the field of HCI.Let's have a look at what is happening in the still relatively exotic world of chatbots. In early spring 2016, Microsoft presented its vision of conversations as a platform, where artificial intelligence (AI) and natural-language interaction allow new ways to experience interactive systems. As a step toward this vision, the company provided a framework for bot developers and launched the first chatbots on Skype. Microsoft, however, is far from alone in holding visions of natural-language interaction. At about the same time, Facebook launched facilities for building chatbots for its messaging application, Messenger. Already, tens of thousands of chatbots have appeared on Messenger. Though the vast majority of these are useful or entertaining for only a tiny group of people-maybe only their developers-some pleasant surprises point to the opportunities ahead. For example, Smokey warns you when the air quality in your city drops below acceptable levels; Instalocate helps you stay updated on your upcoming flights; and Jessie Humani takes you for an entertaining ride through the everyday life of young adults. Then there is Google Assistant, a chatbot integrated in the Allo messaging application and recent versions of the Android operating system that outperforms all the others, though it too can be pretty dumb at times. Google Assistant reliably helps you out with questions in natural language, such as when the sun sets or where to find the nearest coffee shop, even when asked follow-up questions for directions or opening hours. Yet conversations break down fast enough for this to be an interface for only the most enthusiastic of techies.It is no mystery why the major technology companies see chatbots as the next big thing. Mobile messaging applications now have more than 1.5 billion users worldwide [2], and they are competing with social ...

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