Random Forest Model for Intrusion Detection in Crowd-Sourced Reviews

A systematic literature review about the consumers’ side of fake review detection – Which cues do consumers use to determine the veracity of online user reviews?

“…The second most popular category of feature is the reviewer centric type (Barbado, Araque, & Iglesias, 2019;Fazzolari, Buccafurri, Lax, & Petrocchi, 2020;Fontanarava, Pasi, & Viviani, 2017;Gera, Thakur, & Singh, 2015;Goswami, Park, & Song, 2017;Harris, 2018;Jindal & Liu, 2008;Y. Li, Feng, & Zhang, 2016;Pillala, Bhansali, Reddy, & Rojamani, 2020;Pokharkar, Shete, & Ghogare, 2017, p. 7;Shehnepoor, Salehi, Farahbakhsh, & Crespi, 2017;Wang et al, 2016;. These features assess aspects of the user that posted a review, rather than the content of the review itself.…”

“…Reviewer centric features include trust features (Barbado et al, 2019;Fazzolari et al, 2020;Goswami et al, 2017;Jindal & Liu, 2008). An example of trust features are personal features of the reviewer (Barbado et al, 2019), such as whether they use their real name (Barbado et al, 2019;Pillala et al, 2020). Recent research on reviewer centric features also focuses on the detection of reviews that were written by the same reviewer under different names (Le, Li, & Li, 2022).…”