(Hyper) local news aggregation: designing for social affordances

  • Authors:
  • Andrea Kavanaugh;Samah Gad;Sloane Neidig;Manuel A. Pérez-Quiñones;John Tedesco;Ankit Ahuja;Naren Ramakrishnan

  • Affiliations:
  • Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA;Virginia Tech, Blacksburg, VA

  • Venue:
  • Proceedings of the 13th Annual International Conference on Digital Government Research
  • Year:
  • 2012

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Abstract

Being able to debate, reflect, form opinions, consider counter evidence and make informed decisions is a foundation of civic life in democratic societies. Government benefits from broad participation in collective decision making in terms of sustainable outcomes (e.g., greater consensus) and quality of life in our cities and communities. These collective decision making capabilities are undermined by a strange combination of diminishing (or extinct) local print media, especially local newspapers, and by its obverse, a plethora of information and communication opportunities that are scattered across numerous disparate and decentralized websites and resources (e.g., webpages, RSS feeds, social network software, email, listservs, podcasts, tweets). To address this problem, a number of news aggregators have emerged that capture local content from dispersed sites. To facilitate civic engagement, these sites also need to support social interaction and information exchange. In this paper we review the state of the art in local news aggregation in the US and their support for social affordances (social trust, networks and interaction) that are essential to civic participation. We present a prototype we have developed for local news aggregation that supports social affordances. We summarize briefly the design strategies and techniques (e.g., algorithms) we used to cluster topics and user generated content derived from existing local sources. The prototype should lead to a replicable model for other US communities.