News Reliability Evaluation using Latent Semantic Analysis

The rapid rise and widespread of ‘Fake News’ has severe implications in the society today. Much efforts have been directed towards the development of methods to verify news reliability on the Internet in recent years. In this paper, an automated news reliability evaluation system was proposed. The system utilizes term several Natural Language Processing (NLP) techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), Phrase Detection and Cosine Similarity in tandem with Latent Semantic Analysis (LSA). A collection of 9203 labelled articles from both reliable and unreliable sources were collected. This dataset was then applied random test-train split to create the training dataset and testing dataset. The final results obtained shows 81.87% for precision and 86.95% for recall with the accuracy being 73.33%

A Web Infrastructure for Certifying Multimedia News Content for Fake News Defense

In dealing with altered visual multimedia content, also referred to as fake news, we present a ready-to-deploy extension of the current public key infrastructure (PKI), to provide an endorsement and integrity check platform for newsworthy visual multimedia content. PKI, which is primarily used for Web domain authentication, can directly be utilized with any visual multimedia file. Unlike many other fake news researches that focus on technical multimedia data processing and verification, we enable various news organizations to use our developed program to certify/endorse a multimedia news content when they believe this news piece is truthiness and newsworthy. Our program digitally signs the multimedia news content with the news organization's private key, and the endorsed news content can be posted not only by the endorser, but also by any other websites. By installing a web browser extension developed by us, an end user can easily verify whether a multimedia news content has been endorsed and by which organization. During verification, our browser extension will present to the end user a floating logo next to the image or video. This logo, in the shape of a shield, will show whether the image has been endorsed, by which news organization, and a few more pieces of essential text information of the news multimedia content. The proposed system can be easily integrated to other closed-web system such as social media networks and easily applied to other non-visual multimedia files.Comment: 7 pages, 6 figure

Comparative study on sentimental analysis using machine learning techniques

With the advancement of the Internet and the world wide web (WWW), it is observed that there is an exponential growth of data and information across the internet. In addition, there is a huge growth in digital or textual data generation. This is because users post the reply comments in social media websites based on the experiences about an event or product. Furthermore, people are interested to know whether the majority of potential buyers will have a positive or negative experience on the event or the product. This kind of classification in general can be attained through Sentiment Analysis which inputs unstructured text comments about the product reviews, events, etc., from all the reviews or comments posted by users. This further classifies the data into different categories namely positive, negative or neutral opinions. Sentiment analysis can be performed by different machine learning models like CNN, Naive Bayes, Decision Tree, XgBoost, Logistic Regression etc. The proposed work is compared with the existing solutions in terms of different performance metrics and XgBoost outperforms out of all other methods