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The digital revolution has affected museums in many ways, both directly and indirectly. A major external change is the rise of user‐written reviews; that is, reviews written by museum visitors and posted on the Internet. User‐generated reviews pose challenges to museums, as they are publicly available and largely outside the control of museums. This article discusses research on reviews of accredited museums in London. Our data set consists of all reviews written about 88 London museums that were posted on TripAdvisor during 2014, a total of 22,940 reviews. Using a technique called topic modelling, we describe 19 themes in reviewers' stories of their visits. We find that museum visitors pay attention to the ancillary aspects of their visit: queues, cost, food service, toilets, and activities for children. They make fewer comments on the cultural side of the museum experience. However, these cultural aspects do matter and are associated with positive reviews. We argue that reviewers consider museums as part of a wider leisure sector. The article concludes with a discussion of the implications of our study for museum management and assess the usefulness of user‐generated content as a source of data on museum visitors. [ABSTRACT FROM AUTHOR]

The movements of ideas and content between locations and languages are unquestionably crucial concerns to researchers of the information age, and Twitter has emerged as a central, global platform on which hundreds of millions of people share knowledge and information. A variety of research has attempted to harvest locational and linguistic metadata from tweets to understand important questions related to the 300 million tweets that flow through the platform each day. Much of this work is carried out with only limited understandings of how best to work with the spatial and linguistic contexts in which the information was produced, however. Furthermore, standard, well-accepted practices have yet to emerge. As such, this article studies the reliability of key methods used to determine language and location of content in Twitter. It compares three automated language identification packages to Twitter's user interface language setting and to a human coding of languages to identify common sources of disagreement. The article also demonstrates that in many cases user-entered profile locations differ from the physical locations from which users are actually tweeting. As such, these open-ended, user-generated profile locations cannot be used as useful proxies for the physical locations from which information is published to Twitter. [ABSTRACT FROM AUTHOR]

A random-effects meta-analysis of research concerning the door-in-the-face (DITF) influence strategy provides evidence supporting more confident generalizations about the role of several moderator variables than that provided by previous reviews. Variations in the identity of the requester, the identity of the beneficiary, the prosocialness of the requests, the medium of communication, and the time interval between requests all appear to influence the size of DITF effects; variations in concession size do not. DITF effects are small in absolute terms (with an overall mean r of .10), but not remarkably small in the context of other effect sizes concerning social influence. However, there is substantial variability in DITF effects, even under optimal conditions. The review's findings are not easily reconciled with most proposed explanations of DITF effects, but appear consistent with a guilt-based account. [ABSTRACT FROM AUTHOR]