Harnessing collective intelligence in social tagging using Delicious

Abstract

A new collaborative approach in information organization and sharing has recently arisen, known as collaborative tagging or social indexing. A key element of collaborative tagging is the concept of collective intelligence (CI), which is a shared intelligence among all participants. This research investigates the phenomenon of social tagging in the context of CI with the aim to serve as a stepping‐stone towards the mining of truly valuable social tags for web resources. This study focuses on assessing and evaluating the degree of CI embedded in social tagging over time in terms of two‐parameter values, number of participants, and top frequency ranking window. Five different metrics were adopted and utilized for assessing the similarity between ranking lists: overlapList, overlapRank, Footrule, Fagin's measure, and the Inverse Rank measure. The result of this study demonstrates that a substantial degree of CI is most likely to be achieved when somewhere between the first 200 and 400 people have participated in tagging, and that a target degree of CI can be projected by controlling the two factors along with the selection of a similarity metric. The study also tests some experimental conditions for detecting social tags with high CI degree. The results of this study can be applicable to the study of filtering social tags based on CI; filtered social tags may be utilized for the metadata creation of tagged resources and possibly for the retrieval of tagged resources.

How users employ various popular tags to annotate resources in social tagging: An empirical study

“…Combining methods such as regression analysis and clustering analysis, the growth patterns of social tags were examined based on time series data. The examined growth patterns include the sustainability and stabilization of tag distributions (Ma, ), the changes over time in the functionality of social tagging (Oh & Monge, ), and the form of collective intelligence in social tagging (Kamel Boulos & Wheeler, ; O'Reilly & Battelle, ; Wu, Gordon, DeMaagd, & Fan, ; Yi, ).…”

Seven dimensions of contemporary participation disentangled

“…Participation can be interpreted surprisingly broadly: from highly skilled time‐consuming forms of participation such as writing a software device driver for Linux or organizing a multi‐city protest, to low‐effort or even no‐effort forms such as making a comment on a blog, tagging a document with a keyword, or strengthening a search algorithm simply by using it. In information, communication, and media studies participation is relevant to social tagging, social bookmarking, user‐generated content, communities of practice, fan fiction, participatory culture, and the relationship between amateurs and experts (Arazy, Yeo, & Nov, ; Ding et al, ; Fish & Srinivasan, ; Jenkins, Purushotma, Weigel, & Robison, ; Jenkins, , ; Nov, Naaman, & Ye, ; Postigo, ; Rosenbaum & Shachaf, ; Wenger, ; Xu, Ma, Chen, & Ma, ; Yi, ). In some cases the concept of participation is confounded with democracy or democratization, and in places it is used interchangeably with cooperation, collaboration, engagement, or access—but rarely is it explicitly singled out with the question: What is it and how does it work?…”

The development and validation of a one‐bit comparison for evaluating the maturity of tag distributions in a Web 2.0 environment

“…Consensus obtained under social influence is one method of representing the power of participatory collaboration among a crowd of users, which is called collective intelligence (CI; O'Reilly, ). Several researchers (e.g., Yi, ) have stated that this is central to the success and popularity of Web 2.0.…”

“…Web 2.0 is available to everyone; therefore, anyone with access to the Internet can create tags. In social tag‐centered information organization applications, knowing the value of each social tag as an indexing term is crucial (Yi, ).…”

“…Several recent studies have examined the quality of tags. Yi () empirically proved and reported threshold values for the stability of social tagging focusing on resource‐level tags, to help determine the conditions necessary for discerning what collected social tags are valuable for indexing tagged web resources. Syn and Spring () confirmed the potential of using social tags as classificatory metadata by proposing metrics to filter tag noise and leave tags of high quality in a collection.…”