The impact of image descriptions on user tagging behavior: A study of the nature and functionality of crowdsourced tags

Crowdsourcing has emerged as a way to harvest social wisdom from thousands of volunteers to perform a series of tasks online. However, little research has been devoted to exploring the impact of various factors such as the content of a resource or crowdsourcing interface design on user tagging behavior. Although images' titles and descriptions are frequently available in image digital libraries, it is not clear whether they should be displayed to crowdworkers engaged in tagging. This paper focuses on offering insight to the curators of digital image libraries who face this dilemma by examining (i) how descriptions influence the user in his/her tagging behavior and (ii) how this relates to the (a) nature of the tags, (b) the emergent folksonomy, and (c) the findability of the images in the tagging system. We compared two different methods for collecting image tags from Amazon's Mechanical Turk's crowdworkers - with and without image descriptions. Several properties of generated tags were examined from different perspectives: diversity, specificity, reusability, quality, similarity, descriptiveness, and so on. In addition, the study was carried out to examine the impact of image description on supporting users' information seeking with a tag cloud interface. The results showed that the properties of tags are affected by the crowdsourcing approach. Tags from the "with description" condition are more diverse and more specific than tags from the "without description" condition, while the latter has a higher tag reuse rate. A user study also revealed that different tag sets provided different support for search. Tags produced "with description" shortened the path to the target results, whereas tags produced without description increased user success in the search task

An experimental analysis of suggestions in collaborative tagging

Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource, like a web-page. In this paper we investigate the influence of tag suggestions on the emergence of power-law distributions as a result of collaborative tag behavior. Although previous research has already shown that power-laws emerge in tagging systems, the cause of why power-law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power-laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. This imitation behavior leads to a feedback loop in which some tags are reinforced and get more popular which is also known as the `rich get richer' or a preferential attachment model. We present experimental results that show that the power-law distribution forms when tag suggestions are not presented to the users, and the power-law distribution does not hold when there are tag suggestions presented to the user. Furthermore, we show that the real effect of tag suggestions is rather subtle; the power-law distribution that would naturally occur without tag suggestions is `compressed' if tag suggestions are given to the user, resulting in a shorter long tail and a `compressed' top of the power-law distribution. The consequences of this experiment show that tag suggestions by themselves do not account for the formation of power-law distributions in tagging systems

An experimental analysis of suggestions in collaborative tagging

Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource, such as web-page. In this paper we investigate the influence of tag suggestions on the emergence of power-law distributions as a result of collaborative tag behavior. Although previous research has already shown that power-laws emerge in tagging systems, the cause of why power-law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power-laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. This imitation behavior leads to a feedback loop in which some tags are reinforced, which is also known as the 'rich get richer' or a preferential attachment model. We present experimental results showing that the power-law distribution forms when tag suggestions are not presented to the users, and the power-law distribution does not hold when tag suggestions are presented to the user. Furthermore, we show that the real effect of tag suggestions is rather subtle; the power-law distribution that would naturally occur without tag suggestions is 'compressed' if tag suggestions are given to the user, resulting in a shorter long tail and a 'compressed' top of the power-law distribution. We show that tag suggestions by themselves do not account for the formation of power-law distributions in tagging systems

An Experimental Analysis of Suggestions in Collaborative Tagging

Abstract—Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource, like a web-page. In this paper we investigate the influence of tag suggestions on the emergence of power-law distributions as a result of collaborative tag behavior. Although previous research has already shown that power-laws emerge in tagging systems, the cause of why power-law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power-laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. This imitation behavior leads to a feedback loop in which some tags are reinforced and get more popular which is also known as the ‘rich get richer ’ or a preferential attachment model. We present experimental results that show that the power-law distribution forms when tag suggestions are not presented to the users, and the power-law distribution does not hold when there are tag suggestions presented to the user. Furthermore, we show that the real effect of tag suggestions is rather subtle; the power-law distribution that would naturally occur without tag suggestions is ‘compressed ’ if tag suggestions are given to the user, resulting in a shorter long tail and a ‘compressed ’ top of the power-law distribution. The consequences of this experiment show that tag suggestions by themselves do not account for the formation of power-law distributions in tagging systems. Keywords-Distributed information systems; Information retrieval; User interfaces I