Accessing Information with Tags: Search and Ranking

Bullock, Beate Navarro; Hotho, Andreas; Stumme, Gerd

doi:10.1007/978-3-319-90092-6_9

Cited by 12 publications

(2 citation statements)

References 90 publications

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“…For examples of research using network tag analysis, see Ahmed and Lugovic (2019), Buarki and Alkhateeb (2018), Ewbank (2015). For other examples of LIS studies looking at tagging in wider contexts, Navarro Bullock et al (2018) discuss how effective tags are when accessing information; Samanta and Rath (2020) compare the usefulness of tags and controlled subject headings on LibraryThing and Szuban (2018) investigates a videogame wiki to appraise how the use of a folksonomy can complement the top-down work of a hierarchical structure. Sarkar and Bhattacharya (2020) give an excellent overview of comparative studies of folksonomies and controlled vocabularies within LIS.…”

Section: Literature Review and Rationalementioning

confidence: 99%

Tag analysis as a tool for investigating information behaviour: comparing fan-tagging on Tumblr, Archive of Our Own and Etsy

Price

Robinson

2021

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PurposeThis article describes the third part of a three-stage study investigating the information behaviour of fans and fan communities, the first stage of which is described in the study by Price and Robinson (2017).Design/methodology/approachUsing tag analysis as a method, a comparative case study was undertaken to explore three aspects of fan information behaviour: information gatekeeping; classifying and tagging and entrepreneurship and economic activity. The case studies took place on three sites used by fans–Tumblr, Archive of Our Own (AO3) and Etsy. Supplementary semi-structured interviews with site users were used to augment the findings with qualitative data.FindingsThese showed that fans used tags in a variety of ways quite apart from classification purposes. These included tags being used on Tumblr as meta-commentary and a means of dialogue between users, as well as expressors of emotion and affect towards posts. On AO3 in particular, fans had developed a practice called “tag wrangling” to mitigate the inherent “messiness” of tagging. Evidence was also found of a “hybrid market economy” on Etsy fan stores. From the study findings, a taxonomy of fan-related tags was developed.Research limitations/implicationsFindings are limited to the tagging practices on only three sites used by fans during Spring 2016, and further research on other similar sites are recommended. Longitudinal studies of these sites would be beneficial in understanding how or whether tagging practices change over time. Testing of the fan-tag taxonomy developed in this paper is also recommended.Originality/valueThis research develops a method for using tag analysis to describe information behaviour. It also develops a fan-tag taxonomy, which may be used in future research on the tagging practices of fans, which heretofore have been a little-studied section of serious leisure information users.

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Section: Literature Review and Rationalementioning

confidence: 99%

Tag analysis as a tool for investigating information behaviour: comparing fan-tagging on Tumblr, Archive of Our Own and Etsy

Price

Robinson

2021

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“…It's about finding social information that responds to the user. We distinguish, for example, the information retrieval in blogs, microblogs [21,29] and the search for conversations [31]; 2) the second concerns the exploitation of social contents to improve IR, in which social information is used to improve the information retrieval process, for example, tags in folksonomies have been found useful for improving web search and personalized search [18], re-ranking of search results [9,17] and 3) the third paradigm concerns the search for information carried out by several people, collaborative search [44].…”

Section: Social Information Retrievalmentioning

confidence: 99%

Exploring Differences in the Impact of Users’ Traces on Arabic and English Facebook Search

Badache

2019

IEEE/WIC/ACM International Conference on Web Intelligence

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This paper proposes an approach on Facebook search in Arabic and English, which exploits several users' traces (e.g. comment, share, reactions) left on Facebook posts to estimate their social importance. Our goal is to show how these social traces (signals) can play a vital role in improving Arabic and English Facebook search. Firstly, we identify polarities (positive or negative) carried by the textual signals (e.g. comments) and non-textual ones (e.g. the reactions love and sad) for a given Facebook posts. Therefore, the polarity of each comment expressed in Arabic or in English on a given Facebook post, is estimated on the basis of a neural sentiment model. Secondly, we group signals according to their complementarity using attributes (features) selection algorithms. Thirdly, we apply learning to rank (LTR) algorithms to re-rank Facebook search results based on the selected groups of signals. Finally, experiments are carried out on 13,500 Facebook posts, collected from 45 topics, for each of the two languages. Experiments results reveal that Random Forests was the most effective LTR approach for this task, and for the both languages. However, the best appropriate features selection algorithms are ReliefFAttributeEval and InfoGainAttributeEval for Arabic and English Facebook search task, respectively.

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Privacy in Social Information Access

Knijnenburg

2018

Social Information Access

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Accessing Information with Tags: Search and Ranking

Cited by 12 publications

References 90 publications

Tag analysis as a tool for investigating information behaviour: comparing fan-tagging on Tumblr, Archive of Our Own and Etsy

Tag analysis as a tool for investigating information behaviour: comparing fan-tagging on Tumblr, Archive of Our Own and Etsy

Exploring Differences in the Impact of Users’ Traces on Arabic and English Facebook Search

Privacy in Social Information Access

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