A novel group decision making method to overcome the Web 2.0 challenges

With the appearance of Web 2.0 technologies, the way Internet is conceived has dramatically changed. Internet users have begun to perform a more active role in providing and sharing the information available on the Web. Thanks to mobile technologies, users can access the Internet from their smartphones at any time, independently of their location. Moreover, users gather in communities where they can communicate and share information. These communities can hold a high number of people and, consequently, a large amount of information need to be managed. Nevertheless, as the information tends to be disorganized, it is difficult for users to manage them properly to make the most of it. Therefore, there is a necessity of tools that can help users to organize and manage in a proper way the large amount of available information. In this paper, we propose the use of a novel group decision making approach to allow a high number of people to communicate among themselves and reach conclusions in a regulated way. As a high amount of people usually implies too much information, we propose the use of Fuzzy Ontologies as a way to deal with it in an organized way

Unfolding the characteristics of incentivized online reviews

The rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely true. Therefore, review systems emerged to provide more trustworthy sources of information, since customer opinions may be less biased. However, the need to control the consumers’ opinion increased once sellers realized the importance of reviews and their direct impact on sales. One of the methods often used was to offer customers a specific product in exchange for an honest review. Yet, these incentivized reviews bias results and skew the overall rating of the products. The current study uses a data mining approach to predict whether or not a new review published was incentivized based on several review features such as the overall rating, the helpfulness rate, and the review length, among others. Additionally, the model was enriched with sentiment score features of the reviews computed through the VADER algorithm. The results provide an in-depth understanding of the phenomenon by identifying the most relevant features which enable to differentiate an incentivized from a non-incentivized review, thus providing users and companies with a simple set of rules to identify reviews that are biased without any disclaimer. Such rules include the length of a review, its helpfulness rate, and the overall sentiment polarity score.info:eu-repo/semantics/acceptedVersio

A text-mining based model to detect unethical biases in online reviews: a case-study of Amazon.com

The rapid growth of social media in the last decades led e-commerce into a new era of value co-creation between the seller and the consumer. Since there is no contact with the product, people have to rely on the description of the seller, knowing that sometimes it may be biased and not entirely truth. Therefore, reviewing systems emerged in order to provide more trustworthy sources of information, since customer opinions may be less biased. The problem was, once sellers realized the importance of reviews and their direct impact on sales, the need to control this key factor arose. One of the methods developed was to offer customers a certain product in exchange for an honest review. However, in the light of the results of some studies, these "honest" reviews were proved to be biased and skew the overall rating of the product. The purpose of this work is to find patterns in these incentivized reviews and create a model that may predict whether a new review is biased or not. To study this subject, besides the sentiment analysis performed on the data, some other characteristics were taken into account, such as the overall rating, helpfulness rate, review length and the timestamp when the review was written. Results show that some of the most significant characteristics when predicting an incentivized review are the length of a review, its helpfulness rate and the overall polarity score, calculated through VADER algorithm, as the most important sentiment-related factor.O rápido crescimento das redes sociais nas últimas décadas levaram o comércio electrónico a uma nova era de co-criação de valor entre o vendedor e o consumidor. Uma vez que não há contacto com o produto, os clientes têm de se basear na descrição do vendedor, mesmo sabendo que por vezes tal descrição pode ser tendenciosa e não totalmente verdadeira. Deste modo, surgiu um sistema de reviews com o propósito de disponibilizar um meio de informação de maior confiança, uma vez que se trata de partilha de informação entre clientes e por isso mais imparcial. No entanto, quando os vendedores se aperceberam da importância das "reviews" e o seu impacto direto nas vendas, surgiu a necessidade de controlar este fator chave. Uma das formas de o fazer foi através da oferta de determinados produtos em troca de "reviews" honestas. Contudo, à luz dos resultados de alguns estudos, foi demonstrado que estas "reviews" "honestas" são tendenciosas e enviesam a classificação geral do produto. O objetivo deste estudo foi o de encontrar padrões na forma como estas "reviews" incentivadas são escritas e criar um modelo para prever se uma determinada review seria enviesada. Para esta análise, além da análise de sentimentos realizada sobre os dados, outras características foram tidas em conta, tal como a classificação geral, a taxa de "helpfulness", o tamanho da "review" e a hora a que foi escrita. Os modelos gerados mostraram que as características mais importantes na previsão de parcialidade numa "review" são o tamanho e a taxa de utilidade e como característica sentimental mais relevante a pontuação geral da "review", calculada através do algoritmo VADER