@inproceedings{ames2007motivations, abstract = {Why do people tag? Users have mostly avoided annotating media such as photos -- both in desktop and mobile environments -- despite the many potential uses for annotations, including recall and retrieval. We investigate the incentives for annotation in Flickr, a popular web-based photo-sharing system, and ZoneTag, a cameraphone photo capture and annotation tool that uploads images to Flickr. In Flickr, annotation (as textual tags) serves both personal and social purposes, increasing incentives for tagging and resulting in a relatively high number of annotations. ZoneTag, in turn, makes it easier to tag cameraphone photos that are uploaded to Flickr by allowing annotation and suggesting relevant tags immediately after capture. A qualitative study of ZoneTag/Flickr users exposed various tagging patterns and emerging motivations for photo annotation. We offer a taxonomy of motivations for annotation in this system along two dimensions (sociality and function), and explore the various factors that people consider when tagging their photos. Our findings suggest implications for the design of digital photo organization and sharing applications, as well as other applications that incorporate user-based annotation.}, acmid = {1240772}, address = {New York, NY, USA}, author = {Ames, Morgan and Naaman, Mor}, booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems}, doi = {10.1145/1240624.1240772}, interhash = {bd24c17d66d2b904b3fc9444c2b64b44}, intrahash = {c3840b12cf9592a782a09ab9e1bdf49e}, isbn = {978-1-59593-593-9}, location = {San Jose, California, USA}, numpages = {10}, pages = {971--980}, publisher = {ACM}, series = {CHI '07}, title = {Why We Tag: Motivations for Annotation in Mobile and Online Media}, url = {http://doi.acm.org/10.1145/1240624.1240772}, year = 2007 } @article{bonzi1991motivations, abstract = {The citation motivations among 51 self citing authors in several natural science disciplines were investigated. Results of a survey on reasons for both self citation and citation to others show that there are very few differences in motivation, and that there are plausible intellectual grounds for those differences which are substantial. Analysis of exposure in text reveals virtually no differences between self citations and citations to others. Analysis of individual disciplines also uncover no substantive differences in either motivation or exposure in text.}, author = {Bonzi, Susan and Snyder, H.W.}, doi = {10.1007/BF02017571}, interhash = {b531a253fae4751735918d6d5c8b44bd}, intrahash = {fcd88cce5ca6a7c99cb4726921752a1b}, issn = {0138-9130}, journal = {Scientometrics}, language = {English}, number = 2, pages = {245-254}, publisher = {Kluwer Academic Publishers}, title = {Motivations for citation: A comparison of self citation and citation to others}, url = {http://dx.doi.org/10.1007/BF02017571}, volume = 21, year = 1991 } @inproceedings{korner2010categorizers, abstract = {While recent research has advanced our understanding about the structure and dynamics of social tagging systems, we know little about (i) the underlying motivations for tagging (why users tag), and (ii) how they influence the properties of resulting tags and folksonomies. In this paper, we focus on problem (i) based on a distinction between two types of user motivations that we have identified in earlier work: Categorizers vs. Describers. To that end, we systematically define and evaluate a number of measures designed to discriminate between describers, i.e. users who use tags for describing resources as opposed to categorizers, i.e. users who use tags for categorizing resources. Subsequently, we present empirical findings from qualitative and quantitative evaluations of the measures on real world tagging behavior. In addition, we conducted a recommender evaluation in which we study the effectiveness of each of the presented measures and found the measure based on the tag content to be the most accurate in predicting the user behavior closely followed by a content independent measure. The overall contribution of this paper is the presentation of empirical evidence that tagging motivation can be approximated with simple statistical measures. Our research is relevant for (a) designers of tagging systems aiming to better understand the motivations of their users and (b) researchers interested in studying the effects of users' tagging motivation on the properties of resulting tags and emergent structures in social tagging systems}, acmid = {1810645}, address = {New York, NY, USA}, author = {K\"{o}rner, Christian and Kern, Roman and Grahsl, Hans-Peter and Strohmaier, Markus}, booktitle = {Proceedings of the 21st ACM Conference on Hypertext and Hypermedia}, doi = {10.1145/1810617.1810645}, interhash = {ccca64b638181c35972c71e586ddc0c2}, intrahash = {87e3f9fa38eed6342454dcf47bb3e575}, isbn = {978-1-4503-0041-4}, location = {Toronto, Ontario, Canada}, numpages = {10}, pages = {157--166}, publisher = {ACM}, series = {HT '10}, title = {Of Categorizers and Describers: An Evaluation of Quantitative Measures for Tagging Motivation}, url = {http://doi.acm.org/10.1145/1810617.1810645}, year = 2010 } @phdthesis{mcnee2006meeting, abstract = {In order to build relevant, useful, and effective recommender systems, researchers need to understand why users come to these systems and how users judge recommendation lists. Today, researchers use accuracy-based metrics for judging goodness. Yet these metrics cannot capture users' criteria for judging recommendation usefulness. We need to rethink recommenders from a user's perspective: they help users find new information. Thus, not only do we need to know about the user, we need to know what the user is looking for. In this dissertation, we explore how to tailor recommendation lists not just to a user, but to the user's current information seeking task. We argue that each recommender algorithm has specific strengths and weaknesses, different from other algorithms. Thus, different recommender algorithms are better suited for specific users and their information seeking tasks. A recommender system should, then, select and tune the appropriate recommender algorithm (or algorithms) for a given user/information seeking task combination. To support this, we present results in three areas. First, we apply recommender systems in the domain of peer-reviewed computer science research papers, a domain where users have external criteria for selecting items to consume. The effectiveness of our approach is validated through several sets of experiments. Second, we argue that current recommender systems research in not focused on user needs, but rather on algorithm design and performance. To bring users back into focus, we reflect on how users perceive recommenders and the recommendation process, and present Human-Recommender Interaction theory, a framework and language for describing recommenders and the recommendation lists they generate. Third, we look to different ways of evaluating recommender systems algorithms. To this end, we propose a new set of recommender metrics, run experiments on several recommender algorithms using these metrics, and categorize the differences we discovered. Through Human-Recommender Interaction and these new metrics, we can bridge users and their needs with recommender algorithms to generate more useful recommendation lists.}, address = {Minneapolis, MN, USA}, advisor = {Konstan, Joseph A.}, author = {Mcnee, Sean Michael}, interhash = {aa770067601fafb29655af4e21e47422}, intrahash = {4e3e619f4cdda96257b37eac6bb38899}, isbn = {978-0-542-83429-5}, school = {University of Minnesota}, title = {Meeting User Information Needs in Recommender Systems}, year = 2006 } @inproceedings{heckner2009personal, address = {San Jose, CA, USA}, author = {Heckner, Markus and Heilemann, Michael and Wolff, Christian}, booktitle = {Int'l AAAI Conference on Weblogs and Social Media (ICWSM)}, interhash = {f954e699dc6ca2d0abbe5f6ebe166dc7}, intrahash = {d1074484ea350ad88400fe4fc6984874}, month = may, title = {Personal Information Management vs. Resource Sharing: Towards a Model of Information Behaviour in Social Tagging Systems}, year = 2009 } @article{strohmaier2012understanding, abstract = {While recent progress has been achieved in understanding the structure and dynamics of social tagging systems, we know little about the underlying user motivations for tagging, and how they influence resulting folksonomies and tags. This paper addresses three issues related to this question. (1) What distinctions of user motivations are identified by previous research, and in what ways are the motivations of users amenable to quantitative analysis? (2) To what extent does tagging motivation vary across different social tagging systems? (3) How does variability in user motivation influence resulting tags and folksonomies? In this paper, we present measures to detect whether a tagger is primarily motivated by categorizing or describing resources, and apply these measures to datasets from seven different tagging systems. Our results show that (a) users’ motivation for tagging varies not only across, but also within tagging systems, and that (b) tag agreement among users who are motivated by categorizing resources is significantly lower than among users who are motivated by describing resources. Our findings are relevant for (1) the development of tag-based user interfaces, (2) the analysis of tag semantics and (3) the design of search algorithms for social tagging systems.}, author = {Strohmaier, Markus and Körner, Christian and Kern, Roman}, doi = {10.1016/j.websem.2012.09.003}, interhash = {0b972aa7d8892d70761ba3ba11a737f6}, intrahash = {5c063dc162f38895336d2775507132ee}, issn = {1570-8268}, journal = {Web Semantics: Science, Services and Agents on the World Wide Web}, number = 0, pages = {1 - 11}, title = {Understanding why users tag: A survey of tagging motivation literature and results from an empirical study}, url = {http://www.sciencedirect.com/science/article/pii/S1570826812000820}, volume = 17, year = 2012 }