Do You Really Know if It’s True? How Asking Users to Rate Stories Affects Belief in Fake News on Social Media

Research shows that consuming ratings influences purchase decisions in e-commerce and also has modest effects on belief in news articles on social media. We find that the act of producing ratings reduces belief in news articles on social media and induces social media users to think more critically. We propose this intervention as a method to encourage users to realize that, unlike in the product rating setting, social media users who submit their ratings for news articles typically lack firsthand knowledge of the events reported in the news, making it difficult for most users to rate news articles accurately. We asked 68 social media users to assess the believability of 42 social media articles and measured their cognitive activity using electroencephalography. We found that asking users to rate articles using a self-referential question induced them to think more critically—as indicated by increased activation in the medial prefrontal cortex and dorsolateral prefrontal cortex—and made them less likely to believe the articles. The effect extended to subsequent articles; after being asked to rate an article, users were less likely to believe other articles that followed it whether they were asked to rate them or not.

An algorithmic-based multi-attribute decision making model under intuitionistic fuzzy environment

An outranking procedure for Multi-Attribute Decision-Making (MADM) problems is introduced in our work that acts as a decision-aid in recommending the products to the buyers. The buyer’s product assessment is taken as Interval-Valued Intuitionistic Fuzzy Sets (IVIFS) in each attribute. The confidence level that is implicit in the buyer’s product rating is explicated in the proposed work using fuzzy entropy. As the confidence level of the buyer on the product assessment is for both satisfaction and reluctance, it is suitably distributed in membership and non-membership parts of IVIFS. Our work generates a dominance matrix that represents partial or full dominance of one product over another after scoring the products that are unified with buyer’s confidence. The proposed work suggests the product ranking after ascertaining the buyer’s flexibility. An algorithm is written in our work to validate the procedure developed. We have compared our work with other similar works to highlight the benefits of the proposed work. A numerical example is illustrated to highlight the procedure developed.

Identifying the polarity of a text given the emotion of its author

The rise of digital communication systems provides an almost infinite source of information that can be useful to feed classification algorithms, so it makes use of an already categorized collection of opinions of the social network Twitter for the formation and generation of a model of classification of short texts; which aims to categorize the emotional tone found in an author’s Spanish-language digital text. In addition, linguistic, lexicographic and opinion mining computational tools are used to implement a series of methods that allow to automatically finding coincidences or orientations that allow determining the polarity of sentences and categorize them as positive, negative or neutral considering their lemmas. The results obtained from the analysis of emotions and polarity of this project, on the test phrases allow to observe a direct relationship between the categorized emotional tone and it is positive, negative or neutral classification, which allows to provide additional information to know the intention that the author had when he created the sentence. Determining these characteristics can be useful as a consistent information objective that can be leveraged by sectors where the prevalence of a product or service depends on user opinion, product rating or turns with satisfaction metrics.

A Survey on Different Techniques for Product Fake Review Detection and Product Rating

Numerous online business sites empower the customers to create a product reviews along with feedback in the shape of ratings. This gives the organization work force a sign about their items’ remaining on the lookout, while likewise empowering individual customer to frame an assessment and help buy an item. As of late, Sentiment Analysis (SA) has gotten quite possibly interesting due to the potential business advantages of text analysis. One of the most important problems in confronting SA is the manner by which to remove feelings in the assessment, as well as how to identify counterfeit good reviews and negative surveys derived from assessment surveys. Besides, the assessment surveys acquired from clients can divided into two categories: positive and negative, which can be utilized by a shopper to choose an item. In this survey, we have thoroughly discussed about fake review detection of products as well as product rating by different SA techniques. Further, we have discussed the research direction in fake review detection and product rating.

Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application

Search and experience goods, as well as vertical and horizontal differentiation, are fundamental concepts of great importance to business operations and strategy. In our paper, we propose a set of theory-grounded data-driven measures that allow us to measure not only product type (search vs. experience and horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. We used product rating data from Amazon.com to illustrate the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Our results also show that, whereas ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Industry practitioners could utilize our approaches to quantitatively measure product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.

Understanding Persuasion Cascades in Online Product Rating Systems: Modeling, Analysis, and Inference

Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google Play Store, and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called “ message-based persuasion ” lead to “ biased ” product ratings in a cascading manner (we call this the persuasion cascade ). This article investigates: (1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights? We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on both synthetic data and real-world data from Amazon and TripAdvisor. Experiment results show that our inference algorithm has a high accuracy. Furthermore, persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.

Comparative Study on Sentiment Analysis Approach for Online Shopping Review

The internet has revolutionized the way most people shop. Flexibility, convenience, products’ variations, better price, and more privacy contribute to the exponential growth of online shopping platforms. However, due to the nature of online shopping, customers are not able to physically test the product before purchasing. They rely on the information given by the seller and previous customers’ ratings to make their decision. Sometimes, the information that is given by sellers may be fraudulent, misleading, or over claim. Many researchers had found out that ratings and other customers’ reviews can be manipulated and did not reflect on the actual customers’ sentiment on the particular product. This research investigates how sentiment analysis can be used as an alternative solution to measure the positive, negative, and neutral feedback of the past reviews. It is to offer more comprehensive way to help the customers make an informed decision for the product that they wish to buy based on the totality of the reviews. This paper makes a comparative study on sentiment analysis methods on online shopping reviews. This can lead to the proposed theoretical framework of an alternative solution for better insight exploration. It is envisaged that this research would benefit the customer in making a better decision when doing online shopping and may act as a feedback mechanism for the seller to provide good products and services. A good product rating can influence many new buyers and increase business revenue and expansion