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Cyberactivism already has a rich history, but over the past decade the participatory web-with its de-centralized information/media sharing, portability, storage capacity, and user-generated content-has reshaped political and social change. Cyberactivism on the Participatory Web examines the impact of these new technologies on political organizing and protest across the political spectrum, from the Arab Spring to artists to far-right groups. Linking new information and communication technologies to possibilities for solidarity and action-as well as surveillance and control-in a context of globa

The importance of user-generated content is growing as media consumption is moving online; yet, investigations of media bias on user-generated content platforms are rare. We develop a novel procedure to detect coverage bias - i.e., bias in the amount of coverage certain topics or issues receive - on user-generated content platforms. We proceed in two steps. First, we focus on a sample of homogeneous observations and control for observable differences. Second, we compare the coverage of our observations between different language versions of the same platform in a difference-in-differences framework, which allows us to disentangle coverage bias from unobserved heterogeneity between observations. We apply our procedure to Wikipedia and examine whether it has a coverage bias in its biographies of German (and French) Members of Parliament (MPs). Our analysis reveals a small to medium size coverage bias against MPs from the center-left parties in Germany and in France. A plausible explanation are partisan contributions to the Wikipedia biographies, as we show by analyzing patterns of authorship and Wikipedia's talk pages for the German case. Practical implications of our results include raising users' awareness of coverage bias when searching for and processing information obtained on user-generated content platforms.

The importance of user-generated content is growing as media consumption is moving online; yet, investigations of media bias on user-generated content platforms are rare. We develop a novel procedure to detect coverage bias – i.e., bias in the amount of coverage certain topics or issues receive – on user-generated content platforms. We proceed in two steps. First, we focus on a sample of homogeneous observations and control for observable differences. Second, we compare the coverage of our observations between different language versions of the same platform in a difference-in-differences framework, which allows us to disentangle coverage bias from unobserved heterogeneity between observations. We apply our procedure to Wikipedia and examine whether it has a coverage bias in its biographies of German (and French) Members of Parliament (MPs). Our analysis reveals a small to medium size coverage bias against MPs from the center-left parties in Germany and in France. A plausible explanation are partisan contributions to the Wikipedia biographies, as we show by analyzing patterns of authorship and Wikipedia's talk pages for the German case. Practical implications of our results include raising users' awareness of coverage bias when searching for and processing information obtained on user-generated content platforms.

The importance of user-generated content is growing as media consumption is moving online; yet, investigations of media bias on user-generated content platforms are rare. We develop a novel procedure to detect coverage bias - i.e., bias in the amount of coverage certain topics or issues receive - on user-generated content platforms. We proceed in two steps. First, we focus on a sample of homogeneous observations and control for observable differences. Second, we compare the coverage of our observations between different language versions of the same platform in a difference-in-differences framework, which allows us to disentangle coverage bias from unobserved heterogeneity between observations. We apply our procedure to Wikipedia and examine whether it has a coverage bias in its biographies of German (and French) Members of Parliament (MPs). Our analysis reveals a small to medium size coverage bias against MPs from the center-left parties in Germany and in France. A plausible explanation are partisan contributions to the Wikipedia biographies, as we show by analyzing patterns of authorship and Wikipedia's talk pages for the German case. Practical implications of our results include raising users' awareness of coverage bias when searching for and processing information obtained on user-generated content platforms.

Gegenstand dieser Arbeit wird die Frage sein, inwiefern es sich bei User-generated Content um eine sinnvolle Ergänzung für die Kommunikationspolitik von Marken handelt. Zudem soll umrissen werden, auf welche Art und Weise er sich bestmöglich in der Marketingkommunikation einsetzen lässt. Ein besonderes Augenmerk wird dabei der UGC-Kommunikation gewidmet werden, die Unternehmen auf ihren markeneigenen Kanälen betreiben. Da es sich hierbei um ein noch relativ junges, spezifisches und im wissenschaftlichen Diskurs nur wenig untersuchtes Themengebiet handelt, sollen strategische Hintergründe und Zielsetzungen beleuchtet werden, die Unternehmen und Marken dazu bewegen, User-generated Content zu fördern und auf eigenen Kanälen auszuspielen.

Gegenstand dieser Arbeit wird die Frage sein, inwiefern es sich bei User-generated Content um eine sinnvolle Ergänzung für die Kommunikationspolitik von Marken handelt. Zudem soll umrissen werden, auf welche Art und Weise er sich bestmöglich in der Marketingkommunikation einsetzen lässt. Ein besonderes Augenmerk wird dabei der UGC-Kommunikation gewidmet werden, die Unternehmen auf ihren markeneigenen Kanälen betreiben. Da es sich hierbei um ein noch relativ junges, spezifisches und im wissenschaftlichen Diskurs nur wenig untersuchtes Themengebiet handelt, sollen strategische Hintergründe und Zielsetzungen beleuchtet werden, die Unternehmen und Marken dazu bewegen, User-generated Content zu fördern und auf eigenen Kanälen auszuspielen.

This article explores the institutionalization of YouTube: its transformation from user-generated content (UGC) – oriented as a virtual village – into a professionally generated content (PGC) video site, especially after being purchased by Google. YouTube has influenced the traditional media environment, but at the same time this new medium imitates the rules of the old media, including legally managed distribution of broadcasting content and smooth links between content and commercials. YouTube constitutes an evolution of the present media milieu, rather than a revolution. On the other hand, the dominance of mainstream media is, to a degree, still compromised in UGC culture. The emancipatory dimension of UGC media (e.g. as democratic, creative outlet with high accessibility and online library potential) is discussed in the conclusion, not losing sight of the technological-economic limitations placed on its continuing promise.