|Title: ||Are raw RSS feeds suitable for broad issue scanning? A science concern case study|
|Citation: ||Journal of the American Society for Information Science and Technology, 57(12): 1644-1654|
|Publisher: ||Wiley InterScience|
|Issue Date: ||2006 |
|Additional Links: ||http://dx.doi.org/10.1002/asi.20334|
|Abstract: ||Broad issue scanning is the task of identifying important public debates arising in a given broad issue; really simple syndication (RSS) feeds are a natural information source for investigating broad issues. RSS, as originally conceived, is a method for publishing timely and concise information on the Internet, for example, about the main stories in a news site or the latest postings in a blog. RSS feeds are potentially a nonintrusive source of high-quality data about public opinion: Monitoring a large number may allow quantitative methods to extract information relevant to a given need. In this article we describe an RSS feed-based coword frequency method to identify bursts of discussion relevant to a given broad issue. A case study of public science concerns is used to demonstrate the method and assess the suitability of raw RSS feeds for broad issue scanning (i.e., without data cleansing). An attempt to identify genuine science concern debates from the corpus through investigating the top 1,000 burst words found only two genuine debates, however. The low success rate was mainly caused by a few pathological feeds that dominated the results and obscured any significant debates. The results point to the need to develop effective data cleansing procedures for RSS feeds, particularly if there is not a large quantity of discussion about the broad issue, and a range of potential techniques is suggested. Finally, the analysis confirmed that the time series information generated by real-time monitoring of RSS feeds could usefully illustrate the evolution of new debates relevant to a broad issue.|
|Description: ||Metadata only|
|Keywords: ||Broad issue scanning|
Really simple syndication
|Appears in Collections: ||Statistical Cybermetrics Research Group|
Statistical Cybermetrics Research Group
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