First Monday is one of the first openly accessible, peer–reviewed journals on the Internet, solely devoted to the Internet. Since its start in May 1996, First Monday has published 1,361 papers in 215 issues; these papers were written by 1,850 different authors. First Monday is indexed in Communication Abstracts, Computer & Communications Security Abstracts, DoIS, eGranary Digital Library, INSPEC, Information Science & Technology Abstracts, LISA, PAIS, and other services.
|This month: April 2014|
Narrative framing of consumer sentiment in online restaurant reviews
The vast increase in online expressions of consumer sentiment offers a powerful new tool for studying consumer attitudes. To explore the narratives that consumers use to frame positive and negative sentiment online, this paper computationally investigate linguistic structure in 900,000 online restaurant reviews. Negative reviews, especially in expensive restaurants, were more likely to use features previously associated with narratives of trauma. Positive reviews employed framings contextualized by expense: inexpensive restaurant reviews use the language of addiction to frame the reviewer as craving fatty or starchy foods. Positive reviews of expensive restaurants were long narratives using long words emphasizing the reviewer’s linguistic capital and focusing on sensory pleasure. The results demonstrate that portraying the self is a key function of reviews and suggests the important role of online reviews in exploring social psychological variables.
|Also this month!|
Towards a methodology for examining Twitter follower accession
The analysis of content and meta–data has long been the subject of most Twitter studies, however such research only tells part of the story of the development of Twitter as a platform. This paper introduces a methodology to determine the growth patterns of individual users of the platform, a technique referred to as follower accession. Through a number of case studies this paper examines the factors which lead to follower growth, and the identification of non–authentic followers.
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