• 한국벤처창업학회:학술대회논문집
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• 2022.11a
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• pp.181-185
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• 2022

고객들은 조작된 온라인 리뷰가 범람하는 가운데 진정성과 가치를 지닌 리뷰를 보고자한다. 귀인 이론(Attribution theory)의 관점에서, 사람들은 리뷰어의 과거 평가 이력을 바탕으로 리뷰가 진정성 있는지를 판단하는 경향이 있다. 이러한 배경에서 본 연구의 목적은 리뷰어의 과거 평점 이력이 조작된 리뷰로 인식하는 것에 어떠한 영향을 미치며, 최종적으로 리뷰 유용성이 어떠한 영향을 미치는지 알아보는 것이다. 제안된 가설을 검증하기 위해 2차 데이터 분석(연구1)과 실험(연구2)을 수행했으며, 두 연구는 일관된 결과를 보여준다. 연구 1은 리뷰어의 과거 평가 이력이 리뷰 유용성에 미치는 영향을 분석하였다. 귀인이론에 근거하면, 사람들은 리뷰를 다른 목적을 가지고 작성되었다고 인식할 경우에 리뷰가 조작되었다고 생각하고, 그 리뷰가 물건이나 서비스의 진정한 가치를 평가하지 않았다고 간주한다. 따라서 해당 리뷰는 유용성이 낮게 평가되는 경향이 있다. 2차 데이터를 분석하기 위해 우리는 Python을 이용한 웹 스크레이퍼를 개발하여 TripAdvisor(TripAdvisor.com)에서 호텔 정보, 리뷰, 리뷰 정보 등의 연구 데이터를 수집하였다. 수집한 890명 리뷰어에 대한 100,621개의 리뷰를 분석하기 위해 음이항 회귀 분석을 수행하였다. 분석 결과, 평균 평점을 낮게 주는 리뷰어의 경우에 리뷰 유용성에 유의미한 영향을 미치지 않는 것으로 나타났다. 사람들은 극단적인 평점을 거의 주지 않는 리뷰어가 작성한 리뷰가 더 도움이 된다고 평가했다. 연구 2는 리뷰어의 과거 평점 이력을 기준으로 리뷰가 조작되었다고 평가하는 사람들의 인식 프로세스를 실험하였다. 실험 결과, 사람들은 리뷰어의 과거 평점 이력이 평균적으로 평점을 낮게 주는 경우에는 리뷰가 의심스럽다고 판단하지 않는 것으로 나타났다. 그리고 사람들은 리뷰어가 대부분 극단적인 평점을 주는 이력이 있다면 해당 리뷰어가 작성한 리뷰가 의심스럽다고 판단하는 것으로 나타났다. 연구2는 사람들이 리뷰어의 과거 평점 이력을 바탕으로 리뷰가 조작되었는지 또는 리뷰가 도움이 되는지 판단하는 경향이 있음을 보여준다. 본 연구는 귀인이론을 바탕으로 리뷰어의 과거 평점 이력이 리뷰 조작성에 대한 인식과 리뷰 유용성에 미치는 영향을 분석하여, 해당 연구분야에 새로운 관점을 추가한 기여점이 있다.

• Knowledge Management Research
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• v.23 no.2
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• pp.253-276
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• 2022

Online reviews attract much attention because they play an essential role in consumer decision-making. Therefore, it is necessary to investigate the review attributes that affect the perceived helpfulness of consumers. However, most previous studies on the helpfulness of online reviews mainly focus on quantitative factors such as review volume and reviewer attributes. Recently, some studies have investigated the impact of review content and linguistic style matching on consumers' purchase decision-making. Those studies show that consumers consider additional review attributes when evaluating reviews in decision-making. To fill the research gap with existing literature, we investigated the impact of review content and linguistic style matching on review helpfulness. Moreover, this study investigated how the reviewers' expertise moderates the effect of the review content and linguistic style matching on the review helpfulness. The empirical results show that positive affective content has a negative effect on the review helpfulness. The negative affective content and linguistic style matching positively affect review helpfulness. Review expertise relieved the impact of negative affective content and linguistic style matching on review helpfulness. According to the mechanism confirmed in this study, online e-commerce companies can achieve corporate sales growth by identifying factors affecting review helpfulness and reflecting them in their marketing strategies.

• Knowledge Management Research
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• v.23 no.3
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• pp.193-212
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• 2022

Many studies have proposed several factors that affect review helpfulness. Previous studies have investigated the effect of quantitative factors (e.g., star ratings) and affective factors (e.g., sentiment scores) on review helpfulness. Online reviews contain titles and contents, but existing studies focus on the review content. However, there is a limitation to investigating the factors that affect review helpfulness based on the review content without considering the review title. However, previous studies independently investigated the effect of review content and title on review helpfulness. However, it may ignore the potential impact of similarity between review titles and content on review helpfulness. This study used text consistency between review titles and content affect review helpfulness based on the mere exposure effect theory. We also considered the role of information clearness, review length, and source reliability. The results show that text consistency between the review title and the content negatively affects the review helpfulness. Furthermore, we found that information clearness and source reliability weaken the negative effects of text consistency on review helpfulness.

• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.186-190
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• 2010

본 논문은 쇼핑몰의 이용 후기 중 광고성 리뷰를 분류해 내는 방법을 제안한다. 여기서 광고성 리뷰는 주로 업체에서 작성하는 것으로 리뷰 안에 광고 내용이 포함되어 있다. 국외 연구 중에는 드물게 오피니언 스팸 문서의 분류 연구가 진행되고 있지만 한국어 상품평으로부터 광고성 리뷰를 분류하는 연구는 아직 이루어지지 않고 있다. 본 논문에서는 Naive Bayes Classifier를 활용하여 광고성 리뷰를 분류하였다. 이때 확률 계산을 위해 사용된 특징 단어는 POS-Tagging+Bigram, POS-Tagging+Unigram, Bigram을 사용하여 추출하였다. 실험 결과는 POS-Tagging+Bigram 방법을 이용하였을 때 광고성 리뷰의 F-Measure가 80.35%로 정확도 높았다.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• Information Systems Review
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• v.23 no.3
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• pp.25-50
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• 2021

Though there have been a lot of studies about online product review, the effects of highlighted reviewhave not been examined enough. Highlighted review is a type of review that the platform designer changes its size or position in order to highlight without any sponsorship or incentive. The main subject of this study is about how highlighted review type affects consumer's perception and behavior in online information acquisition. We collected data from 171 subjects to test hypotheses. Using three different types of screen captures, we compared three groups - general review group, positive highlighted review only group, and both positive and negative highlighted review group. As a result, disclosing both of positiveand negative highlighted review was perceived more useful than disclosing only positive highlighted review. However, correlation between highlighted review type and review skepticism was not statistically significant. The impacts of review usefulness and skepticism on platform credibility were statistically significant, and the correlation between platform credibility and usage intention was also significant. All of results is almost similar across two product types, search goods and experiential goods. This research provides practical implications to online shopping platform designers when they design review systems to make people use their platforms.

• Knowledge Management Research
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• v.22 no.1
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• pp.243-267
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• 2021

Sentiment represents one's own state through the process of change to stimulus, and emotion represents a simple psychological state felt for a certain phenomenon. These two terms tend to be used interchangeably, but their meaning and usage are different. In this study, we try to find out how it affects the helpfulness of reviews by classifying sentiment and emotion through online reviews written by online consumers after purchasing and using various products and services. Recently, online reviews have become a very important factor for businesses and consumers. Helpful reviews play a key role in the decision-making process of potential customers and can be assessed through review helpfulness. The helpfulness of reviews is becoming increasingly important in practice as it is utilized in marketing strategies in business as well as in purchasing decision-making issues of consumers. And academically, the importance of research to find the factors influencing the helpfulness of reviews is growing. In this study, Yelp.com secured reviews on restaurants and conducted a study on how the sentiment and emotion of online reviews affect the helpfulness of reviews. Based on the prior research, a research model including sentiment and emotions for online reviews was built, and text mining analyzes how the sentiment and emotion of online reviews affect the helpfulness of online reviews, and the difference in the effects on emotions It was verified. The results showed that negative sentiment and emotion had a greater effect on review helpfulness, which was consistent with the negative bias theory.

• Knowledge Management Research
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• v.23 no.2
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• pp.229-251
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• 2022

Online reviews are useful for other consumers to make reasonable purchase decisions by providing previous buyers' experiences. However, when online reviewers are biased, online reviews do not accurately reflect the true quality of the product. Therefore, we investigated the characteristics of reviewers with underreporting bias to cope with the problem of declining reliability of online reviews. In this context, this study attempted to examine the characteristics of reviewers with underreporting bias using 14,165 reviews of Steam, an online game platform. As a result of the analysis, reviewers with underreporting bias mainly write reviews positively, write reviews within a short period from the game release date, but tend to write reviews after playing games for longer time, and write reviews when purchasing high-priced games. Since this study has explored the characteristics of reviewers showing underreporting bias, it will be meaningful as a basic study to cope with the problem caused by underreporting bias.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.79-81
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• 2014

People increasingly rely on sources of information from E-commerce reviews. Product reviews is an important determinant of potential customers' buying choices. They are also utilized by product manufacturers to find problems of their products and to collect competitive intelligence information about their competitors. Unfortunately, it is well-known that many online product reviews are not made by genuine costumers of products. Reviewers could write some undeserving positive reviews to promote or fake negative reviews to defame some certain product, and we call them fake product reviews. Fake product review detection makes an attempt to detect fake reviews and removes them to restore the truthful ones for readers. To the best of our knowledge, there is still less published study on this problem. In this paper, we make a survey and an attempt to give a brief overview on fake product review detection. The related work of fake product review detection is presented including web spam and spam email. Then some methods to detect fake reviews are introduced and summarized. The trend of fake product review detection is concluded finally.

• Information Systems Review
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• v.23 no.1
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• pp.45-68
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• 2021

As the influence of online reviews on consumer decision-making increases, concerns about review manipulation are also increasing. Fake reviews or review manipulations are emerging as an important problem by posting untrue reviews in order to increase sales volume, causing the consumer's reverse choice, and acting at a high cost to the society as a whole. Most of the related prior studies have focused on predicting review manipulation through data mining methods, and research from a consumer perspective is insufficient. However, since the possibility of manipulation of reviews perceived by consumers can affect the usefulness of reviews, it can provide important implications for online word-of-mouth management regardless of whether it is false or not. Therefore, in this study, we analyzed whether there is a difference between the review evaluated by the consumer as being manipulated and the general review, and verified whether the manipulated review negatively affects the review usefulness. For empirical analysis, 34,711 online book reviews on the LibraryThing website were analyzed using multilevel logistic regression analysis and Poisson regression analysis. As a result of the analysis, it was found that there were differences in product level, reviewer level, and review level factors between reviews that consumers perceived as being manipulated and reviews that were not. In addition, manipulated reviews have been shown to negatively affect review usefulness.