How TripAdvisor’s reviewers level of expertise influence their online rating behaviour and the usefulness of reviews

The internet has improved the buying behaviour of customers. The development of technologies has led to the dissemination of opinions on social networks where customers buy goods and services. These comments on social networks started to be a part of the purchasing process. Until a few years ago, customers used to choose their itineraries based on tourist guides or brochures. Nowadays, customers’ reviews have changed the way a destination is portrayed, enhancing the description of a product or a service to a level that not even the supplier was able to reach before. There are different types of reviewers. The aim of this study is to identify both reviews, experts and non-expert reviewers and analyse the way they write their reviews. Reviews of five hotels taken from the TripAdvisor website were used in order to conduct this study. After analyzing a great set of variables, the results show that there is not much different on the amount of positive/negative reviews written by a reviewer, however, there is a difference in the deeper meaning of a review when it is positive than when it is negative. The expert reviewer tends to be more emotional when writing positive reviews than negative reviews. Regarding the usefulness of the reviews, there is no significant difference in usefulness of a review whether is an written by an expert reviewer or by a non-expert reviewer. The results also indicate that being an expert does not influence the rating a reviewer gives to a hotel stay either. The study was conducted by using Lexalytics program to analyze a Natural Language Processing (NLP) used to classify reviews according to their polarity. With this study, a new research in study was filled. This study gives insights on the polarity of a review depending on the type of reviewer. The results of this study are also important for hotel managers in order for them to understand the type of guest in house.O desenvolvimento da tecnologia, com ênfase na internet e nos seus desenvolvimentos ao longo dos anos, melhorou o comportamento dos clientes e levou à disseminação de opiniões em redes sociais onde os clientes compram productos e serviços. Os comentários feitos a um produto ou serviço nas redes sociais começaram a fazer parte do processo da compra. Até há uns anos atrás, os clientes escolhiam os itinerários para as suas viagens com base em guias turísticos e brochuras. Recentemente, os comentários de clientes mudaram a maneira que um destino é explicado e ilustrado, melhorando, desta forma, a descrição de um produto/serviço a um nível que nem mesmo os fornecedores destes tinham alcançado ainda. Há diferentes tipos de reviewers. O objectivo deste estudo é identificar ambos tipos, expert e non-expert e analisar o estilo de reviews escrita por estes. Experts são assim denominados se tiverem escrito mais de dez reviews; por outro lado os non-expert reviewers são assim denominados se tiverem escrito menos de 10 reviews. Para este estudo, foi utilizada informação de cinco hotéis de Orlando, Florida, retirada do TripAdvisor. Depois de uma análise das variáveis, os resultados mostram que não há grande diferença no que toca ao volume de comentários positivos/negativos escritos por um utilizador. Por outro lado, existe uma diferença na emoção dada a cada comentário, entre os utilizadores. O expert reviewer tende a ser mais emocional quando escreve comentários positivos do que quando escreve comentários negativos. Relativamente a utilidade de cada comentário, não há grande diferença no que toca a ser um expert reviewer ou um non-expert a escrever um comentário. Os resultados indicam, também, que ser um expert não tem qualquer influência na avaliação que um utilizador dá a sua estadia num hotel. Este estudo foi feito com base no programa Lexalytics, com objectivo de analisar a Natural Language Processing (NLP) usada para classificar os comentários de acordo com a sua polaridade

Drug Reviews: Cross-condition and Cross-source Analysis by Review Quantification Using Regional CNN-LSTM Models

Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are known to perform better than usual machine learning models in the case of textual data sequences. Second, how effective is it to migrate such information extraction models across different drug review data sets and across different disease conditions. Therefore three experiments were designed, first, an In-domain experiment where train and test data are from the same dataset. Two more experiments were conducted to examine the migration capability of models, namely cross-data source, where train and test are from different sources and cross-disease condition model training, where train and test data belong to different disease conditions in the same dataset. The experiments were evaluated using popular metrics such as RMSE, MAE, R2 and Pearson’s coefficient and the results showed that the proposed deep learning regression model works less successfully when compared to the machine learning sentiment extraction models in the literature, which were done on the same datasets. But, this study contributes to the existing literature in the quantity of research work done and in quality of the model and also suggests the future researchers on how to improve. This work also addressed the shortcomings in the literature by introducin

Top Comment or Flop Comment? Predicting and Explaining User Engagement in Online News Discussions

Comment sections below online news articles enjoy growing popularity among readers. However, the overwhelming number of comments makes it infeasible for the average news consumer to read all of them and hinders engaging discussions. Most platforms display comments in chronological order, which neglects that some of them are more relevant to users and are better conversation starters. In this paper, we systematically analyze user engagement in the form of the upvotes and replies that a comment receives. Based on comment texts, we train a model to distinguish comments that have either a high or low chance of receiving many upvotes and replies. Our evaluation on user comments from TheGuardian.com compares recurrent and convolutional neural network models, and a traditional feature-based classifier. Further, we investigate what makes some comments more engaging than others. To this end, we identify engagement triggers and arrange them in a taxonomy. Explanation methods for neural networks reveal which input words have the strongest influence on our model's predictions. In addition, we evaluate on a dataset of product reviews, which exhibit similar properties as user comments, such as featuring upvotes for helpfulness.Comment: Accepted at the International Conference on Web and Social Media (ICWSM 2020); 11 pages; code and data are available at https://hpi.de/naumann/projects/repeatability/text-mining.htm

Conjectural guarantees loom large: evidence from the stock returns of Fannie Mae and Freddie Mac

Fannie Mae and Freddie Mac are government sponsored enterprises (GSEs) with publicly traded equity. Although these companies hold government issued charters, their securities are not legally backed by the full faith and credit of the United States government. Yet, investors and rating agencies seem to believe that the U.S. Government would "bail out" Fannie or Freddie if they became distressed. We provide evidence of a conjectural guarantee in GSE stock returns. Stock that contains an option on returning the shares at a given price to the issuer -- the government, in this case -- show pronounced nonlinearity (convexity) in the sensitivity of its return to market return. Using non parametric methods on daily stock returns, we find that the GSEs' returns are less responsive to market movements the more sharply the market declines. Our findings are consistent with a government guarantee in GSE stock against catastrophic losses but not against atrophic losses.Mortgages ; Financial institutions ; Government-sponsored enterprises

Management Responses to Online Reviews: Big Data From Social Media Platforms

User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competitive advantage and improve customer relationship management by leveraging big data, social media analytics, and deep learning techniques. Since negative reviews' harmful effects are greater than positive comments' contribution, firms must strategise their responses to intervene in and minimise those damages. Although current literature includes a sheer amount of research that presents effective response strategies to negative reviews, they mostly overlook an extensive classification of response strategies. The first study consists of two phases and focuses on comprehensive response strategies to only negative reviews. The first phase is explorative and presents a correlation analysis between response strategies and overall ratings of hotels. It also reveals the differences in those strategies based on hotel class, average customer rating, and region. The second phase investigates effective response strategies for increasing the subsequent ratings of returning customers using logistic regression analysis. It presents that responses involving statements of admittance of mistake(s), specific action, and direct contact requests help increase following ratings of previously dissatisfied returning customers. In addition, personalising the response for better customer relationship management is particularly difficult due to the significant variability of textual reviews with various topics. The second study examines the impact of personalised management responses to positive and negative reviews on rating growth, integrating a novel method of multi-topic matching approach with a panel data analysis. It demonstrates that (a) personalised responses improve future ratings of hotels; (b) the effect of personalised responses is stronger for luxury hotels in increasing future ratings. Lastly, practical insights are provided

Combating Fake News on Social Media: A Framework, Review, and Future Opportunities

Social media platforms facilitate the sharing of a vast magnitude of information in split seconds among users. However, some false information is also widely spread, generally referred to as “fake news”. This can have major negative impacts on individuals and societies. Unfortunately, people are often not able to correctly identify fake news from truth. Therefore, there is an urgent need to find effective mechanisms to fight fake news on social media. To this end, this paper adapts the Straub Model of Security Action Cycle to the context of combating fake news on social media. It uses the adapted framework to classify the vast literature on fake news to action cycle phases (i.e., deterrence, prevention, detection, and mitigation/remedy). Based on a systematic and inter-disciplinary review of the relevant literature, we analyze the status and challenges in each stage of combating fake news, followed by introducing future research directions. These efforts allow the development of a holistic view of the research frontier on fighting fake news online. We conclude that this is a multidisciplinary issue; and as such, a collaborative effort from different fields is needed to effectively address this problem

An Analysis of Programming Course Evaluations Before and After the Introduction of an Autograder

Commonly, introductory programming courses in higher education institutions have hundreds of participating students eager to learn to program. The manual effort for reviewing the submitted source code and for providing feedback can no longer be managed. Manually reviewing the submitted homework can be subjective and unfair, particularly if many tutors are responsible for grading. Different autograders can help in this situation; however, there is a lack of knowledge about how autograders can impact students' overall perception of programming classes and teaching. This is relevant for course organizers and institutions to keep their programming courses attractive while coping with increasing students. This paper studies the answers to the standardized university evaluation questionnaires of multiple large-scale foundational computer science courses which recently introduced autograding. The differences before and after this intervention are analyzed. By incorporating additional observations, we hypothesize how the autograder might have contributed to the significant changes in the data, such as, improved interactions between tutors and students, improved overall course quality, improved learning success, increased time spent, and reduced difficulty. This qualitative study aims to provide hypotheses for future research to define and conduct quantitative surveys and data analysis. The autograder technology can be validated as a teaching method to improve student satisfaction with programming courses.Comment: Accepted full paper article on IEEE ITHET 202

Deep Learning for Text Style Transfer: A Survey

Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. It has a long history in the field of natural language processing, and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. We also provide discussions on a variety of important topics regarding the future development of this task. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_SurveyComment: Computational Linguistics Journal 202