False advertising or slander? Using location based tweets to assess online rating-reliability

Poddar, Amit; Banerjee, Syagnik; Sridhar, Karthik

doi:10.1016/j.jbusres.2017.08.030

Cited by 21 publications

(13 citation statements)

References 14 publications

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“…A star rating (ranging from 1 to 5 stars) is typically included in each online review, and previous studies have noted that consumer-generated ratings have a substantial impact on the success or failure of a product in internet commerce (Chevalier and Mayzlin, 2006 ; Lafky, 2014 ). For example, it has been found that even one extra star in a Yelp review could increase revenues by 5–9% (Economist, 2015 ; Poddar et al, 2017 ). Given the great value of star ratings, online merchants have tried to adopt various marketing strategies to affect online rating behaviors, which may increase the number of false reviews.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Money on Fake Rating Behavior in E-Commerce: Electrophysiological Time Course Evidence From Consumers

Wang

Luo

et al. 2018

Front. Neurosci.

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Online ratings impose significant effects on the behaviors of potential customers. Thus, online merchants try to adopt strategies that affect this rating behavior, and most of these strategies are connected to money, such as the strategies of returning cash coupons if a consumer gives a five-star rating (RI strategy, an acronym for “returning” and “if”) or returning cash coupons directly with no additional requirements (RN strategy, an acronym for “returning” and “no”). The current study explored whether a certain strategy (RN or RI) was more likely to give rise to false rating behaviors, as assessed by event-related potentials. A two-stimulus paradigm was used in this experiment. The first stimulus (S1) was the picture of a product with four Chinese characters that reflected the product quality (slightly defective vs. seriously defective vs. not defective), and the second stimulus (S2) displayed the coupon strategy (RN or RI). The participants were asked to decide whether or not to give a five-star rating. The behavioral results showed that the RI strategy led to a higher rate of five-star ratings than the RN strategy. For the electrophysiological time courses, the N1, N2, and LPP components were evaluated. The slightly defective products elicited a larger amplitude of the N1 component than the seriously defective and not-defective products, reflecting that perceptual difficulty was associated with the processing of the slightly defective products. The RI strategy evoked a less negative N2 and a more positive LPP than the RN strategy, indicating that the subjects perceived less conflict and experienced stronger incentives when processing the RI strategy. These findings will benefit future studies of fake online comments and provide evidence supporting the policy of forbidding the use of the RI strategy in e-commerce.

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Section: Introductionmentioning

confidence: 99%

The Effects of Money on Fake Rating Behavior in E-Commerce: Electrophysiological Time Course Evidence From Consumers

Wang

Luo

et al. 2018

Front. Neurosci.

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“…The user comments in online also unidentified, there are some cases of two false slander and advertising which can made struggle in business. ANEW (Affective norms for English words) were used to remove an emotion score from the location, which were mined by Twitter and Foursquare [21].…”

Section: Related Workmentioning

confidence: 99%

Random Forest with Harmony Search Optimization for Location Based Advertising

Rohilla¹,

Chakraborty²,

Kumar³

2019

IJITEE

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Location based services are essential for the mobile user for obtaining the information based on the specific location. Initially, the location information is obtained using the GIS system for getting accurate information about the location. Then relevant information is extracted based on the location then the features are extracted from the dataset. Extracted features are segmented in order to simplify the process of location based advertising. The segmented features are used for simplifying the processing of retrieved data. The random forest algorithm is utilized to effectively retrieve the user data. It is one of the Meta heuristic algorithms which provide best result for the retrieval process. The optimal solution is needed for the designed algorithm for the purpose of avoiding local optimization, thus it improves the performance with respect to accuracy. The optimization algorithm Harmony search is used for getting optimal result based on the location. The exact information for the query is obtained for the proposed approach based on the location.

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“…Bing et al [8] studied tweets and predicted industry's sector-based stock prices. Poddar et al [9] studied the impact of location-based tweets. Hence, there was a promise in using tweets as raw data in the theory building exercise.…”

Section: Conceptual Developmentmentioning

confidence: 99%

“…The present study fills this important gap. The fact that Twitters API is open to other applications and most of the data is public [9] further positions tweets as interesting input for this study.…”

Section: Conceptual Developmentmentioning

confidence: 99%

“…The twitter user profile is understood to be of younger age groups [9] and such age groups were understood to be more influenced by their own peer groups [11]. The starting premise of the study was to evaluate the influence of the movie on its audience and subsequently creating the positive sentiment on twitter which proved substantial on creating a positive demand for Yakult.…”

Section: Conceptual Developmentmentioning

confidence: 99%

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Qualitative Evidence for a Behavioral Extension of the Expectancy Valence Theory: The Netflix-Twitter-Yakult Case

Varma¹

2018

TEL

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The Netflix movie "To all the boys I've loved before" released in the USA on 17th Aug 2018 was linked to a substantial increase in sales of the probiotic drink Yakult. This study probes the phenomenon with an analysis of tweets for the first three weeks after the release of the movie and also assesses reasons for the phenomenon with the theoretical underpinning of the Expectancy Valence (EV) theory. The first section of the study was conducted by undertaking a probe on 1922 tweets made during 17th Aug 2018 (the release date of the movie in the USA) till 6th September 2018. The second part of the study was conducted by taking intensive in-depth interviews of the target segment after 6th September 2018. The study highlights the growing significance of social media in shaping the sentiments by influencing perception and opinion (valence), which eventually influences the sales of products (expectation). The study found out that the sharp increase in sales was due to positive behavioral sentiment created by the social media.

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False advertising or slander? Using location based tweets to assess online rating-reliability

Cited by 21 publications

References 14 publications

The Effects of Money on Fake Rating Behavior in E-Commerce: Electrophysiological Time Course Evidence From Consumers

The Effects of Money on Fake Rating Behavior in E-Commerce: Electrophysiological Time Course Evidence From Consumers

Random Forest with Harmony Search Optimization for Location Based Advertising

Qualitative Evidence for a Behavioral Extension of the Expectancy Valence Theory: The Netflix-Twitter-Yakult Case

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