Assessing the credibility of online social network messages.

ABSTRACT Information gathered socially online is a key feature of the growth and development of modern society. Presently the Internet is a platform for the distribution of data. Millions of people use Online Social Networks daily as a tool to get updated with social, political, educational or other occurrences. In many cases information derived from an Online Social Network is acted upon and often shared with other networks, without further assessments or judgments. Many people do not check to see if the information shared is credible. A user may trust the information generated by a close friend without questioning its credibility, in contrast to a message generated by an unknown user. This work considers the concept of credibility in the wider sense, by proposing whether a user can trust the service provider or even the information itself. Two key components of credibility have been explored; trustworthiness and expertise. Credibility has been researched in the past using Twitter as a validation tool. The research was focused on automatic methods of assessing the credibility of sets of tweets using analysis of microblog postings related to trending topics to determine the credibility of tweets. This research develops a framework that can assist the assessment of the credibility of messages in Online Social Networks. Four types of credibility are explored (experienced, surface, reputed and presumed credibility) resulting in a credibility hierarchy. To determine the credibility of messages generated and distributed in Online Social Networks, a virtual network is created, which attributes nodes with individual views to generate messages in the network at random, recording data from a network and analysing the data based on the behaviour exhibited by agents (an agent-based modelling approach). The factors considered for the experiment design included; peer-to-peer networking, collaboration, opinion formation and network rewiring. The behaviour of agents, frequency in which messages are shared and used, the pathway of the messages and how this affects credibility of messages is also considered. A framework is designed and the resulting data are tested using the design. The resulting data generated validated the framework in part, supporting an approach whereby the concept of tagging the message status assists the understanding and application of the credibility hierarchy. Validation was carried out with Twitter data acquired through twitter’s Application Programming Interface (API). There were similarities in the generation and frequency of the message distributions in the network; these findings were also recorded and analysed using the framework proposed. Some limitations were encountered while acquiring data from Twitter, however, there was sufficient evidence of correlation between the simulated and real social network datasets to indicate the validity of the framework.N/

Computing word-of-mouth trust relationships in social networks from Semantic Web and Web 2.0 data sources

Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks

Network segregation in a model of misinformation and fact checking

Misinformation under the form of rumor, hoaxes, and conspiracy theories spreads on social media at alarming rates. One hypothesis is that, since social media are shaped by homophily, belief in misinformation may be more likely to thrive on those social circles that are segregated from the rest of the network. One possible antidote is fact checking which, in some cases, is known to stop rumors from spreading further. However, fact checking may also backfire and reinforce the belief in a hoax. Here we take into account the combination of network segregation, finite memory and attention, and fact-checking efforts. We consider a compartmental model of two interacting epidemic processes over a network that is segregated between gullible and skeptic users. Extensive simulation and mean-field analysis show that a more segregated network facilitates the spread of a hoax only at low forgetting rates, but has no effect when agents forget at faster rates. This finding may inform the development of mitigation techniques and overall inform on the risks of uncontrolled misinformation online