Social information seeking
Social information seeking (SIS) is a field of research that involves studying situations, motivations, and methods for people seeking and sharing information in participatory online social sites, such as Yahoo! Answers, Answerbag, WikiAnswers and Twitter as well as building systems for supporting such activities. Highly related topics involve traditional and virtual reference services, information retrieval, information extraction, and knowledge representation.
Social information seeking is often materialized in online question-answering (QA) websites, which are driven by a community. Such QA sites have emerged in the past few years as an enormous market, so to speak, for the fulfillment of information needs. Estimates of the volume of questions answered are difficult to come by, but it is likely that the number of questions answered on social/community QA (cQA) sites far exceeds the number of questions answered by library reference services, which until recently were one of the few institutional sources for such question answering. cQA sites make their content – questions and associated answers submitted on the site – available on the open web, and indexable by search engines, thus enabling web users to find answers provided for previously asked questions in response to new queries.
The popularity of such sites have been increasing dramatically for the past several years. Major sites that provide a general platform for questions of all types include Yahoo! Answers, Answerbag and Quora. While other sites that focus on particular fields; for example, StackOverflow (computing). StackOverflow has 3.45 million questions, 1.3 million users and over 6.86 million answers since July 2008 while Quora has 437 thousand questions, 264 thousand users and 979 thousand answers.
Social Q&A or cQA, according to Shah et al., consists of three components: a mechanism for users to submit questions in natural language, a venue for users to submit answers to questions, and a community built around this exchange. Viewed in that light, online communities have performed a question answering function perhaps since the advent of Usenet and Bulletin Board Systems, so in one sense cQA is nothing new. Websites dedicated to cQA, however, have emerged on the web only within the past few years: the first cQA site was the Korean Naver Knowledge iN, launched in 2002, while the first English-language CQA site was Answerbag, launched in April 2003. Despite this short history, however, cQA has already attracted a great deal of attention from researchers investigating information seeking behaviors, selection of resources, social annotations, user motivations, comparisons with other types of question answering services, and a range of other information-related behaviors.
Some of the interesting and important research questions in this area include:
- What causes people to be involved in social Q&A?
- What is the motivation of people who participate in social Q&A?
- Why do questioners choose social Q&A as a source to find information?
- Why do they ask questions online to people whose background or expertise may be unverified?
- Why do they choose social Q&A over other sources to look for information?
- What do they expect from answers given by anonymous people on the Web?
- Why are the answerers willing to share information and knowledge with anonymous people, for free?
- Why do they spend time and effort to find information and help others online? Why are they willing to expose their personal stories to people and inform others with their experiences?
Shah et al. provide a detailed research agenda for social Q&A.
Friendsourcing is an important component of social question and answering, including how to route questions to friends or others who will most likely answer the question. Paul et al. did a study on question and answering on Twitter, and found that the most popular question types asked on Twitter were rhetorical and factual. Surprisingly, along with entertainment and technology questions, people asked personal and health-related questions. The majority of questions received no response, while a handful of questions received a high number of responses. The larger the askers’ network, the more responses she received; however, posting more tweets or posting more frequently did not increase chances of receiving a response. Most often the ‘follow’ relationship between asker and answerer was one-way.
Paul et al. also examined what factors of the askers would increase the chance of getting a response and found that more relevant responses are received when there is a mutual relationship between askers and answerers. Intuitively, we would expect this, as mutual relationship would indicate stronger tie strength and hence, more number of relevant answers.
In order to recommend the most appropriate users to provide answers in a social network, we need to find approaches to detect users’ authority in a social network. In the field of information retrieval, there has been a trend of research investigating ways to detect users’ authority effectively and accurately in a social network.
Cha et al. investigate possible metrics for determining authority users on popular social network Twitter. They propose the following three simple network-based metrics and discuss their usefulness in determining a user’s influence.
- indegree (followers count)
- retweet count
- mention count
An initial analysis of the three aforementioned metrics showed that the users with the highest indegrees and the users with the highest retweet/mention counts were not the same. The top 1% of users by indegree are shown to have very low correlation with the same percentile of users by retweets and by mentions. This implies that follower count is not useful in determining whether a user’s tweets get retweeted or whether the other users engage with them.
In, Pal et al. designed features to measure a user’s authority on a certain topic. For example, retweet impact refers to how many times this user has been retweeted on this certain topic, and the impact is also dampened by a factor measuring how many times this user has been retweeted by a unique author to avoid the cases when a user has extremely enthusiastic fans who retweet regardless of the content. And they first used a clustering approach to find the target cluster which has the highest average score across all features, and used a ranking algorithm to find the most authoritative users within the cluster.
With these authority detection methods, social Q&A could be more effective in providing accurate answers to askers.
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