| Shrnutí: | <p>Wikipedia is the primary authoritative information resource on the web for billions of people, and perhaps the most important reference work in human history. Its modernised notion of the encyclopaedia is a widely accessible and rapidly updatable record for both historical knowledge and current events. The trove of available trace data from its users' browsing patterns also means that the site acts as an appealing, representative barometer for wider patterns of collective attention.</p>
<p>Studies of news media are often concerned with news values—properties of events—that together define newsworthiness—how likely an event is to be chosen for news coverage. In essence; what makes an event news? Traditional study, however, has frequently focussed on data directly from news media, journalists, or even news sharing on social media rather than independent "extra-media" data. This raises concerns of selection biases, platform-specific network effects, and endogeneity. To counter these issues I turn to Wikipedia and the way its users access its information for an audience-centric perspective on current events, news values, and newsworthiness. </p>
<p>In this thesis I conduct three studies designed around understanding collective response to current events. In the first study, I explore how events are represented on Wikipedia and accessed by is audience. To do this I develop a temporal community detection approach towards identifying the topics primarily browsed by users. Secondly, by combining the extra-media data of Wikipedia with a matched news article database I address and reformulate foundational hypotheses of news values and newsworthiness theory. In the third study, I more directly analyse the peaks of collective attention that emerge when a subject is in the news. I develop a time series clustering algorithm to identify the characteristic shapes of these peaks and propose a forecasting model for their growth and decay.</p>
<p>This thesis makes significant contributions to research on online representations of current events, modern understandings of news value theory, and models of collective attention dynamics. By not directly studying news media itself, this 'altmetric' style approach to studying the impact of news events is an important practical and theoretical advancement. It also has deep implications for newsrooms employing editorial analytics and for how platforms, including Wikipedia, serve content on current events to users. More widely, since newsworthiness and attention influence what information is popular—even acceptable—online, studies from novel, independent settings such as this are crucial to informing future journalistic and online speech policies and regulations.</p>
|
|---|