Fake profile detection techniques in large-scale online social networks: A comprehensive review

The importance of behavioral data to identify online fake reviews for tourism businesses: a systematic review

“…Some of these studies (e.g., Banerjee & Chua, 2014;Cardoso, Silva & Almeida, 2018;Chang et al, 2015;Hunt, 2015;Lappas, Sabnis & Valkanas, 2016a;Lappas, Sabnis & Valkanas, 2016b;Li, Feng & Zhang, 2016;Munzel, 2016) focus on the Tourism industry category, while others fall into the hospitality industry category (Chen, Guo & Deng, 2014;Li et al, 2014;Li et al, 2018;Luca & Zervas, 2016). Some works (Lin et al, 2014;Zhang et al, 2016;Ramalingam & Chinnaiah, 2018) were included as part of the analysis because their results can be implemented in every industry that allows consumers to write reviews, including the tourism industry. Elmurngi & Gherbi (2018) analyzed false reviews in E-commerce, considering that TripAdvisor is the most important e-commerce platform in the hospitality industry; therefore, this study might be of interest to the present study (Reyes-Menendez, .…”

“…In this methodology, comments are classified as positive, negative or neutral according to the words contained in them (Chen, Guo & Deng, 2014;Elmurngi & Gherbi, 2017). The third direction of research comprises other methodologies that aim to either discover new knowledge to be implemented in false review detection for tourism businesses (Hunt, 2015) or perform the analysis of legal aspects and measures that countries like the United Kingdom or Australia take to counteract false reviews (Ramalingam & Chinnaiah, 2018). Table 2 provides a classification of the studies reviewed in the present study into the aforementioned three groups.…”

“…The results suggest that false reviews predominate in profiles with low scores, and that the more followers a community has, the greater the number of false reviews Lin et al (2014) This study proposes a new approach to identifying false reviews that is based on the content of the reviews and the behavior of the users. The results show that the proposed approach is more precise and accurate than current algorithms All Industries Ramalingam & Chinnaiah (2018) This study reviews the latest algorithms of false profile detection in social networks All Industries Zhang et al (2016) This study analyzes non-verbal characteristics of users who write false reviews to create a predictive algorithm of detection of false reviews. The algorithm can complement the traditional method of detection of false reviews…”

Detection of malicious social bots: A survey and a refined taxonomy

“…Tables (6,7,8) provide a summary of datasets and corresponding findings of the previously studied methods. It is worth mentioning that we did not include details related to datasets used in graph-based approaches as they already were provided in Ramalingam and Chinnaiah (2018). Light Gradient Boosting Machine (LGBM) achieved an accuracy of 97%.…”

Typification of Impersonated Accounts on Instagram

“…This work [3] presents a novel technique to discriminate real accounts on social networks from fake ones. The writers from this [4] study provide a review of existing and state-of-the-art Sybil detection methods with an introductory approach and present some of the emerging open issues for Sybil detection in Online Social Networks. #post 44 11 27 66 56 68 183 35 10 8 12 6 #comment 130K 175K 50K 20K 53K 90K 100K 79K 211K 196K 16K 22K #like 3.9 M 5.08M 880K 520K 1.76M 1.60M 4M 1.9M 14.5M 14.27M 1.24M 6030K crawled #profile a 500K 500K 500K 500K 500K 500K 500K 500K 500K 500K 500K 500K a 500K randomly selected profiles from users who have reacted in the shape of Like and Comment.…”