Social reader : following social networks in the wilds of the blogosphere

The social interactions manifest in blogs by the network of comments left by owners and readers are an under-used resource, both for blog pundits and industry. We present a web-based feed reader that renders these relationships with a graph representation, and enables exploration by displaying people and blogs who are proximate to a user's network. Social Reader is an example of Casual Information Visualization, and aims to help the user understand and explore blog-based social networks in a daily, real-life setting. A six week study of the software involving 20 users confirmed the usefulness of the novel visual display, via a quantitative analysis of use logs, and an exit survey.

Augmenting trust establishment in dynamic systems with social networks

Social networking has recently flourished in popularity through the use of social websites. Pervasive computing resources have allowed people stay well-connected to each other through access to social networking resources. We take the position that utilizing information produced by relationships within social networks can assist in the establishment of trust for other pervasive computing applications. Furthermore, we describe how such a system can augment a sensor infrastructure used for event observation with information from mobile sensors (ie, mobile phones with cameras) controlled by potentially untrusted third parties.

Emotional reactions to real-world events in social networks

A convergence of emotions among people in social networks is potentially resulted by the occurrence of an unprecedented event in real world. E.g., a majority of bloggers would react angrily at the September 11 terrorist attacks. Based on this observation, we introduce a sentiment index, computed from the current mood tags in a collection of blog posts utilizing an affective lexicon, potentially revealing subtle events discussed in the blogosphere. We then develop a method for extracting events based on this index and its distribution. Our second contribution is establishment of a new bursty structure in text streams termed a sentiment burst. We employ a stochastic model to detect bursty periods of moods and the events associated. Our results on a dataset of more than 12 million mood-tagged blog posts over a 4-year period have shown that our sentiment-based bursty events are indeed meaningful, in several ways.

The role of health literacy and social networks in arthritis patients' health information-seeking behavior : a qualitative study

Background. Patients engage in health information-seeking behaviour to maintain their wellbeing and to manage chronic diseases such as arthritis. Health literacy allows patients to understand available treatments and to critically appraise information they obtain from a wide range of sources.

Aims. To explore how arthritis patients' health literacy affects engagement in arthritis-focused health information-seeking behaviour and the selection of sources of health information available through their informal social network.

Methods. An exploratory, qualitative study consisting of one-on-one semi-structured interviews. Twenty participants with arthritis were recruited from community organizations. The interviews were designed to elicit participants' understanding about their arthritis and arthritis medication and to determine how the participants' health literacy informed selection of where they found information about their arthritis and pain medication.

Results. Participants with low health literacy were less likely to be engaged with health information-seeking behaviour. Participants with intermediate health literacy were more likely to source arthritis-focused health information from newspapers, television, and within their informal social network. Those with high health literacy sourced information from the internet and specialist health sources and were providers of information within their informal social network.

Conclusion. Health professionals need to be aware that levels of engagement in health information-seeking behaviour and sources of arthritis-focused health information may be related to their patients' health literacy.

Recognising user identity in twitter social networks via text mining

Social networks have become a convenient and effective means of communication in recent years. Many people use social networks to communicate, lead, and manage activities, and express their opinions in supporting or opposing different causes. This has brought forward the issue of verifying the owners of social accounts, in order to eliminate the effect of any fake accounts on the people. This study aims to authenticate the genuine accounts versus fake account using writeprint, which is the writing style biometric. We first extract a set of features using text mining techniques. Then, training of a supervised machine learning algorithm to build the knowledge base is conducted. The recognition procedure starts by extracting the relevant features and then measuring the similarity of the feature vector with respect to all feature vectors in the knowledge base. Then, the most similar vector is identified as the verified account.