Event Estimation Accuracy of Social Sensing with Facebook for Social Internet of Vehicles

ABSTRACT

Social Internet of Vehicles (SIoV) is a new paradigm that enables social relationships a vehicles via the Internet. People in the vehicles using Online Social Networks (OSNs) can be an integral part of SIoV that enables the collection of data for sensing a physical phenomenon, i.e., social sensing. In this paper, we study the main social sensing mechanism in Facebook, Comment Thread Network (CTN), which is based on the interactions of users through user walls in Facebook for SIoV. After seeing their commuters’ contents about an event, users either add comments or like these posts, and Facebook CTN emerges as a social sensing medium in estimation of an event through social consensus. For the first time, this study investigates the social sensing capability of Facebook CTN, i.e., the accuracy of collective observations for SIoV. The accuracy depends on the user characteristics and the features of the OSN, since perceptions of the users and how they use Facebook may manipulate their observation signals. We analyze the reliability of Facebook CTN for varying user behaviors, user relationships, Facebook features, and network size. The results indicate that the polarized weighting of the observations and the use of less reliable post types in CTN deteriorate the accuracy of the estimate signal, i.e., social consensus. Furthermore, the selection of users is likely to be an important factor in social sensing.

 

 

 

EXISATING  SYSTEM :

Social sensing with CTN is radically different from the existing sensing schemes due to several factors, which stem mainly from the social sensors, i.e., Facebook users along with the features of Facebook. Observation signals are generated by Facebook users through CTN, and they include noise compo Nent due to users’ perceptions. Moreover, sharing observation signals in various forms in the comment thread may introduce additional noise. After all, studying social sensing mechanisms and the social sensors in OSNs will foster the research on OSNs, and the gained insights are likely to contribute the effective designs of future OSNs along with social sensing applications in many areas in our lives mainly including disaster management, online marketing and many more to come in future.

PROPOSED SYSTEM :

In this paper, we study the main social sensing mechanism in Facebook, Comment Thread Network (CTN), which is based on the interactions of users through user walls in Facebook for SIoV. After seeing their commuters’ contents about an event, users either add comments or like these posts, and Facebook CTN emerges as a social sensing medium in estimation of an event through social consensus. For the first time, this study investigates the social sensing capability of Facebook CTN, i.e., the accuracy of collective observations for SIoV. The accuracy depends on the user characteristics and the features of the OSN, since perceptions of the users and how they use Facebook may manipulate their observation signals. We analyze the reliability of Facebook CTN for varying user behaviors, user relationships, Facebook features, and network size.

CONCLUSION :

CTN is the main mechanism for social sensing in Facebook, which is actively used by Facebook users during the course of major social events for estimating an event of interest in SIoV. Understanding the reliability of estimation made out of collective user observations in CTN is significant due to applications of social sensing in numerous aspects of human lives. In this study, by developing an analytical social sensing model for user observations in CTN from a signal processing perspective, in which we model Facebook users as social sensors and their observations as social signals by incorporating their perceptions in SIoV, we analyze the reliability of Facebook CTN for varying user behaviors and relationships, Facebook features and network size. The results indicate that while the characteristics of the event signal affect the accuracy of the social sensing, also the polarized weighting of the observations and use of less reliable post types in CTN during social sensing are important in the sense that they deteriorate the accuracy of the estimate signal, i.e., social consensus. Furthermore, user relationships inside the community may alter the reliability of social sensing implying that the selection of OSN users in social sensing is essential in obtaining an accurate estimation. In our future research, we aim to investigate and understand the effect of friendship correlations between the users for various types of events and also the effect of liar users in Facebook CTN on the estimate signal. To enhance our model, we will also consider more options beside like such as sad, angry, love, etc.