Inverse Contexture Abstractive Term Frequency Model Using Surf Scale Diffusive Neural Network for Analysis of Fake Social Content in Public Forum

Abstract

Increasingly heterogeneous information on social discussion forums, fake news arises to create rumors to change the reliability of information resources due to contextual terms miss classification. The problem is that features definitions and their relational contexts are not properly extracted to analyze the contextual terms. The Original sense of contextual terms is affected by lexical terms, interrogative terms, extortion, semantic features, and sarcastic terms. To concentrate originality, subjective terms are extracted as positive credibility correlation scores between positive and negative content ratios. To propose a Fake content analysis based on Inverse Contexture Abstractive Term Frequency Model using Surf Scale Diffusive Neural Network for public forum social content. By analyzing the positive correlation of the sentence using Reliable Subjective Influence Score (RSIS), this selects the positive terms depends on mutual content dependencies between the originality terms. To analyze the Inverse Contexture Abstractive Term Frequency Model (ICATFM) for feature selection to select the credibility score in the sentence to relate to subjective real or non-real terms. The selected features are trained into a deep neural classifier optimized with Surf Scale Diffusive Neural Network (S2DNN). This implementation proves the best performance by identifying the fake detection to produce higher precision and recall rate to increase the classification accuracy compared to other methods.