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Social reader: towards browsing the social web

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Abstract

We describe Social Reader, a feed-reader-plus-social-network aggregator that mines comments from social media in order to display a user’s relational neighborhood as a navigable social network. Social Reader’s network visualization enhances mutual awareness of blogger communities, facilitates their exploration and growth with a fully dragn- drop interface, and provides novel ways to filter and summarize people, groups, blogs and comments. We discuss the architecture behind the reader, highlight tasks it adds to the workflow of a typical reader, and assess their cost. We also explore the potential of mood-based features in social media applications. Mood is particularly relevant to social media, reflecting the personal nature of the medium. We explore two prototype mood-based features: colour coding the mood of recent posts according to a valence/arousal map, and a mood-based abstract of recent activity using image media. A six week study of the software involving 20 users confirmed the usefulness of the novel visual display, via a quantitative analysis of use logs, and an exit survey.

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Notes

  1. http://www.alexa.com/siteinfo/facebook.com#—Accessed May 2011

  2. http://www.alexa.com/topsites

  3. Typical is one industry contact’s interest in knowing the influential bloggers in their parenting site of 20,000+ users.

  4. http://blog-research.ivec.org

  5. Now an online trend discovery service at blogpulse.com.

  6. www.pi-werk.de/projects/interactive/linkbar+forensik.pdf—Accessed May 2011

  7. www.moodstats.com

  8. www.wefeelfine.org

  9. Alternatively, lower latency updates can be achieved by using a ping server, such as weblogs.com.

  10. http://code.google.com/p/pyrfeed/wiki/GoogleReaderAPI

  11. Many blog platforms have the option to publish a feed of the latest comments for a blog or post, but not all of them, and even where possible, some bloggers do not publish these feeds. Social Reader attempts to detect comment feeds and use them if available.

  12. Blogger, Livejournal, and Wordpress, to name a few.

  13. http://code.google.com/apis/ajaxfeeds/

  14. In reality, isolated nodes and sub-networks do exist, formed by blogs that do not allow comments, and closed communities that only allow internal URL attributions, respectively.

  15. http://code.google.com/apis/ajaxfeeds/

  16. http://code.google.com/apis/socialgraph/

  17. Dunbar’s Number, ∼150, provides quasi-scientific support for what is common sense: relationships beyond some number are increasingly less likely to be meaningful or stable.

  18. OPML is an XML file format used by a number of feed readers for the importing and exporting of lists of feed subscriptions.

  19. Selecting an owned URL will display all feed items originating at that site, which is useful if the site publishes feed pre-filtered, for example, by author or topic. This feature requires mapping items from the disparate feeds into a comprehensive site image, and is future work.

  20. Latecomers saw the user set peak at 25.

  21. A portion of the survey was borrowed from [32], an interesting analysis of the place of blogs in the media ecology.

  22. Yi et al.’s intent taxonomy, used in Section 4.5, has an evaluative function. In our case, the fact that we were unable to map any low-level interactions to the Reconfigure intent, points up a deficiency, which these comments also expose.

  23. http://www.intac.net/breakdown-of-the-blogosphere/—Accessed May 2011

  24. Another candidate for primary concept is behaviour, e.g., “feeds for when I only have a few minutes” (james.wheare.org/notes/2009/07/towards-manageable-feed-reader.php)

  25. These questions were in part inspired by [47].

  26. “More flexibility in changing the onscreen layout” was the equal top requested feature in the user study cited above.

  27. www.boxesandarrows.com/view/forgotten_forefather_paul_otlet—Accessed May 2011

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Acknowledgement

The authors wish to thank Thin Nguyen for his constructive conversation about the role of mood in browsing social media, and for implementing the text mood classifier.

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Authors and Affiliations

  1. Department of Computing, Curtin University, Bld. 314, Kent St. Bentley, Western Australia, 6102, Australia

    Brett Adams

  2. Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Victoria, 3220, Australia

    Dinh Phung & Svetha Venkatesh

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Correspondence to Brett Adams.

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Adams, B., Phung, D. & Venkatesh, S. Social reader: towards browsing the social web. Multimed Tools Appl 69, 951–990 (2014). https://doi.org/10.1007/s11042-012-1138-5

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