Identification of Deception Detection on Social Media (Twitter) Data Sets using Naive Base Classification and RVNN Model

Abstract

Twitter being a famous social media site not only helps people to share their thoughts in microblogs but also plays a pivotal role in situations of emergency for communication, announcement and so on. However, it results in anaversive effect when inappropriate tweet is reposted or shared to people thereby spreading rumors. This work describesthe methodologies in identifying the rumors using specific attributes like precision, fi-score, recall and support thereby solving the ranging rumor issues across the social media platform. A system detects candidate's rumor from twitter and then evaluates it applicably. The result of experiment shows the proposed algorithm in order to detect the rumors with acceptable accuracy.

Rumor detection technology based on ubiquitous relationship

“…The baseline models include both handcrafted detection algorithms and deep learning detection algorithms, providing a comprehensive and scientific perspective for the experiment. In addition, we compare the experimental research methodology of this study with four other methods(SVM-TS [13], RvNN [14], PPC_RNN+CNN [15], PLAN [16], Bi-GCN [6]), aiming to demonstrate the performance of each model on the Twitter-based Datasets. The rumor detection results are presented in Table 1 and Figure 3.…”