Ruogu Kang

Semantic imitation in social tagging

We present a semantic imitation model of social tagging and exploratory search based on theories of cognitive science. The model assumes that social tags evoke a spontaneous tag-based topic inference process that primes the semantic interpretation of resource contents during exploratory search, and the semantic priming of existing tags in turn influences future tag choices. The model predicts that (1) users who can see tags created by others tend to create tags that are semantically similar to these existing tags, demonstrating the social influence of tag choices; and (2) users who have similar information goals tend to create tags that are semantically similar, but this effect is mediated by the semantic representation and interpretation of social tags. Results from the experiment comparing tagging behavior between a social group (where participants can see tags created by others) and a nominal group (where participants cannot see tags created by others) confirmed these predictions. The current results highlight the critical role of human semantic representations and interpretation processes in the analysis of large-scale social information systems. The model implies that analysis at both the individual and social levels are important for understanding the active, dynamic processes between human knowledge structures and external folksonomies. Implications on how social tagging systems can facilitate exploratory search, interactive information retrievals, knowledge exchange, and other higher-level cognitive and learning activities are discussed.

A Semantic Imitation Model of Social Tag Choices

We describe a semantic imitation model of social tagging that integrates formal representations of semantics and a stochastic tag choice process to explain and predict emergent behavioral patterns. The model adopts a probabilistic topic model to separately represent external word-topic and internal word-concept relations. These representations are coupled with a tag-based topic inference process that predicts how existing tags may influence the semantic interpretation of a document. The inferred topics influence the choice of tags assigned to a document through a random utility model of tag choices. We show that the model is successful in explaining the stability in tag proportions across time and power-law frequency-rank distributions of tag co-occurrences for semantically general and narrow tags. The model also generates novel predictions on how emergent behavioral patterns may change when users with different domain expertise interact with a social tagging system. The model demonstrates the weaknesses of single-level analyses and highlights the importance of adopting a multi-level modeling approach to explain online social behavior.

Exploratory information search by domain experts and novices

The arising popularity of social tagging system has the potential to transform traditional web search into a new era of social search. Based on the finding that domain expertise could influence search behavior in traditional search engines, we hypothesized and tested the idea that domain expertise would have similar influence on search behavior in a social tagging system. We conducted an experiment comparing search behavior of experts and novices when they searched using a tradition search engine and a social tagging system. Results from our experiment showed that experts relied more on their own domain knowledge to generate search queries, while novices were influenced more by social cues in the social tagging system. Experts were also found to conform to each other more than novices in their choice of bookmarks and tags. Implications on the design of future social information systems are discussed.

Strangers on Your Phone: Why People Use Anonymous Communication Applications

Anonymity online is important to people at times in their lives. Anonymous communication applications such as Whisper and YikYak enable people to communicate with strangers anonymously through their smartphones. We report results from semi-structured interviews with 18 users of these apps. The goal of our study was to identify why and how people use anonymous apps, their perceptions of their audience and interactions on the apps, and how these apps compare with other online social communities. We present a typology of the content people share, and their motivations for participation in anonymous apps. People share various types of content that range from deep confessions and secrets to lighthearted jokes and momentary feelings. An important driver for participation and posting is to get social validation from others, even though they are anonymous strangers. We also find that participants believe these anonymous apps allow more honesty, openness, and diversity of opinion than they can find elsewhere. Our results provide implications for how anonymity in mobile apps can encourage expressiveness and interaction among users.

Facilitating exploratory search by model-based navigational cues

We present an extension of a computational cognitive model of social tagging and exploratory search called the semantic imitation model. The model assumes a probabilistic representation of semantics for both internal and external knowledge, and utilizes social tags as navigational cues during exploratory search. We used the model to generate a measure of information scent that controls exploratory search behavior, and simulated the effects of multiple presentations of navigational cues on both simple information retrieval and exploratory search performance based on a previous model called SNIF-ACT. We found that search performance can be significantly improved by these model-based presentations of navigational cues for both experts and novices. The result suggested that exploratory search performance depends critically on the match between internal knowledge (domain expertise) and external knowledge structures (folksonomies). Results have significant implications on how social information systems should be designed to facilitate knowledge exchange among users with different background knowledge.

Exploiting knowledge-in-the-head and knowledge-in-the-social-web: effects of domain expertise on exploratory search in individual and social search environments

Our study compared how experts and novices performed exploratory search using a traditional search engine and a social tagging system. As expected, results showed that social tagging systems could facilitate exploratory search for both experts and novices. We, however, also found that experts were better at interpreting the social tags and generating search keywords, which made them better at finding information in both interfaces. Specifically, experts found more general information than novices by better interpretation of social tags in the tagging system; and experts also found more domain-specific information by generating more of their own keywords. We found a dynamic interaction between knowledge-in-the-head and knowledge-in-the-social-web that although information seekers are more and more reliant on information from the social Web, domain expertise is still important in guiding them to find and evaluate the information. Implications on the design of social search systems that facilitate exploratory search are also discussed.

Do collaborators' annotations help or hurt asynchronous analysis

Our study investigated the use of annotations in an asynchronous crime-solving task. In Study 1, regardless of whether they anticipated a partner, participants had better performance if they annotated more about connections across documents. In Study 2, annotations that pointed to more connections across documents improved the performance of the second participant. Annotations that pointed to few connections across documents hurt performance, especially when people were more aware of their partners. This research suggests that future collaborative tools should help people discern useful from useless annotations.

Conformity out of Diversity: Dynamics of Information Needs and Social Influence of Tags in Exploratory Information Search

We studied the dynamic effects of information needs and social influence of tags in an exploratory search task. Although initially differences in information needs led to diversity in tag choices, this diversity disappeared as participants collaboratively tagged the same set of resources. Our findings are in general consistent with the notion that people conform to the collective interpretation of contents in an information system. In addition, our results showed that conformity does not only arise out of imitation of behavior, but also from the same underlying semantic interpretation or knowledge structures of users as they engage in informal collaboration through the social tagging system. Implications for design of social information system are discussed.

User perceived quality of online social information services: from the perspective of knowledge management

Features of online social information services show promises for overcoming obstacles in current knowledge management practices. This paper first discussed the potential efficacy and emerging practices of such technologies in the domain of knowledge management. Then a quality model of online social information systems was derived from prior literature on online information service quality and analyses of characteristics of emerging technologies. An online questionnaire was developed and administrated to 168 users. Four quality dimensions that are perceived as important by users were identified by factor analysis and proved to be reliable: system usability, content quality, content exchangeability and accessibility, and sociability. The findings of this research provide implications for developers of both enterprise knowledge management systems and public social Websites, and can facilitate future development of the instrument measuring the quality of online social service from other perspectives.