Making the news interesting: understanding the relationship between familiarity and interest

ABSTRACT

News feeds are an important element of information encountering, feeding our (new) interests but also leading to a state of information overload. Current solutions often select information similar to the user's interests. However, long-term interest in one topic, and being highly familiar with that topic, does not necessarily imply an actual interest response will occur when more of the same topic is selected. This study explores how important familiarity is in predicting an interest response. In a study with 30 subjects, interest was manipulated by topical familiarity using novel stimuli from a popular news source. This study shows, within this context, familiarity is moderately important for an interest response: familiarity does indeed make the news interesting, but only to a certain extent. The results set a baseline for predicting interest during information encountering, indicating familiarity is important, but not the only influential variable a system should consider when selecting information for users.

References

1. Barry, C. L. (1994). User-defined relevance criteria: An exploratory study. Journal of the American Society for Information Science, 45(3): 149--159.
2. Billsus, D. and Pazzani, M. (2007). Adaptive news access. In Brusilovsky, P., Kobsa, A., and Nejdl, W., editors, The Adaptive Web: Methods and Strategies of Web Personalization, volume 4321 of Lecture Notes in Computer Science, pages 550--570. Springer, Berlin / Heidelberg.
3. Denning, P. J. (2006). Infoglut. Commun. ACM, 49(7): 15--19.
4. Glassey, R. and Azzopardi, L. (2011). Finding interest in the stream. Proceedings of the American Society for Information Science and Technology, 48(1): 1--4.
5. Kintsch, W. (1980). Learning from text, levels of comprehension, or: Why anyone would read a story anyway. Poetics, 9(1--3): 87--98.
6. Konstan, J. and Riedl, J. (2012). Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction, 22: 101--123.
7. Schraw, G. and Lehman, S. (2001). Situational interest: A review of the literature and directions for future research. Educational Psychology Review, 13: 23--52(30).
8. Silvia, P. J. (2001). Interest and interests: The psychology of constructive capriciousness. Review of General Psychology, 5(3): 270--290.
9. Silvia, P. J. (2006). Exploring the psychology of interest. Oxford University Press, New York.
10. Silvia, P. J. (2008). Appraisal components and emotion traits: Examining the appraisal basis of trait curiosity. Cognition & Emotion, 22(1): 94--113.
11. Sonnenwald, D. H. (1999). Perspectives of human information behaviour: contexts, situations, social networks and information horizons. In Wilson, T. D. and Allen, D. K., editors, Exploring the contexts of information behaviour, pages 176--190. Taylor Graham Publishing, London, UK.
12. Van den Broek, E. L., Van der Sluis, F., and Schouten, T. E. (2010). User-centered digital preservation of multimedia. ERCIM (European Research Consortium for Informatics and Mathematics) News, 80: 45--47.
13. Wade, S. E., Buxton, W. M., and Kelly, M. (1999). Using think-alouds to examine reader-text interest. Reading Research Quarterly, 34(2): 194--216.
14. Wixted, J. and Stretch, V. (2004). In defense of the signal detection interpretation of remember/know judgments. Psychonomic Bulletin & Review, 11: 616--641.
15. Xu, Y. C. and Chen, Z. (2006). Relevance judgment: What do information users consider beyond topicality? J. Am. Soc. Inf. Sci. Technol., 57(7): 961--973.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Philosophical/theoretical foundations of artificial intelligence

      • Cognitive science

• Human-centered computing

  • Human computer interaction (HCI)

• Information systems

  • Information retrieval

    • Retrieval tasks and goals

      • Document filtering

      • Information extraction