Semantic Features-Based Discourse Analysis Using Deceptive and Real Text Reviews
Social media usage for news, feedback on services, and even shopping is increasing. Hotel services, food cleanliness and staff behavior are also discussed online. Hotels are reviewed by the public via comments on their websites and social media accounts. This assists potential customers before they...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| author | Husam M. Alawadh Amerah Alabrah Talha Meraj Hafiz Tayyab Rauf |
| author_facet | Husam M. Alawadh Amerah Alabrah Talha Meraj Hafiz Tayyab Rauf |
| author_sort | Husam M. Alawadh |
| collection | DOAJ |
| description | Social media usage for news, feedback on services, and even shopping is increasing. Hotel services, food cleanliness and staff behavior are also discussed online. Hotels are reviewed by the public via comments on their websites and social media accounts. This assists potential customers before they book the services of a hotel, but it also creates an opportunity for abuse. Scammers leave deceptive reviews regarding services they never received, or inject fake promotions or fake feedback to lower the ranking of competitors. These malicious attacks will only increase in the future and will become a serious problem not only for merchants but also for hotel customers. To rectify the problem, many artificial intelligence–based studies have performed discourse analysis on reviews to validate their genuineness. However, it is still a challenge to find a precise, robust, and deployable automated solution to perform discourse analysis. A credibility check via discourse analysis would help create a safer social media environment. The proposed study is conducted to perform discourse analysis on fake and real reviews automatically. It uses a dataset of real hotel reviews, containing both positive and negative reviews. Under investigation is the hypothesis that strong, fact-based, realistic words are used in truthful reviews, whereas deceptive reviews lack coherent, structural context. Therefore, frequency weight–based and semantically aware features were used in the proposed study, and a comparative analysis was performed. The semantically aware features have shown strength against the current study hypothesis. Further, holdout and k-fold methods were applied for validation of the proposed methods. The final results indicate that semantically aware features inspire more confidence to detect deception in text. |
| format | Article |
| id | doaj-art-c3a3f160703a49d29517f27a3b1edfbd |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
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| series | Information |
| spelling | doaj-art-c3a3f160703a49d29517f27a3b1edfbd2025-01-20T01:58:30ZengMDPI AGInformation2078-24892023-01-011413410.3390/info14010034Semantic Features-Based Discourse Analysis Using Deceptive and Real Text ReviewsHusam M. Alawadh0Amerah Alabrah1Talha Meraj2Hafiz Tayyab Rauf3Department of English Language and Translation, College of Languages and Translation, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Computer Science, COMSATS University Islamabad—Wah Campus, Wah Cantt 47040, PakistanIndependent Researcher, Bradford BD8 0HS, UKSocial media usage for news, feedback on services, and even shopping is increasing. Hotel services, food cleanliness and staff behavior are also discussed online. Hotels are reviewed by the public via comments on their websites and social media accounts. This assists potential customers before they book the services of a hotel, but it also creates an opportunity for abuse. Scammers leave deceptive reviews regarding services they never received, or inject fake promotions or fake feedback to lower the ranking of competitors. These malicious attacks will only increase in the future and will become a serious problem not only for merchants but also for hotel customers. To rectify the problem, many artificial intelligence–based studies have performed discourse analysis on reviews to validate their genuineness. However, it is still a challenge to find a precise, robust, and deployable automated solution to perform discourse analysis. A credibility check via discourse analysis would help create a safer social media environment. The proposed study is conducted to perform discourse analysis on fake and real reviews automatically. It uses a dataset of real hotel reviews, containing both positive and negative reviews. Under investigation is the hypothesis that strong, fact-based, realistic words are used in truthful reviews, whereas deceptive reviews lack coherent, structural context. Therefore, frequency weight–based and semantically aware features were used in the proposed study, and a comparative analysis was performed. The semantically aware features have shown strength against the current study hypothesis. Further, holdout and k-fold methods were applied for validation of the proposed methods. The final results indicate that semantically aware features inspire more confidence to detect deception in text.https://www.mdpi.com/2078-2489/14/1/34credibility checkdiscourse analysisfrequency featuressemantically aware features |
| spellingShingle | Husam M. Alawadh Amerah Alabrah Talha Meraj Hafiz Tayyab Rauf Semantic Features-Based Discourse Analysis Using Deceptive and Real Text Reviews Information credibility check discourse analysis frequency features semantically aware features |
| title | Semantic Features-Based Discourse Analysis Using Deceptive and Real Text Reviews |
| title_full | Semantic Features-Based Discourse Analysis Using Deceptive and Real Text Reviews |
| title_fullStr | Semantic Features-Based Discourse Analysis Using Deceptive and Real Text Reviews |
| title_full_unstemmed | Semantic Features-Based Discourse Analysis Using Deceptive and Real Text Reviews |
| title_short | Semantic Features-Based Discourse Analysis Using Deceptive and Real Text Reviews |
| title_sort | semantic features based discourse analysis using deceptive and real text reviews |
| topic | credibility check discourse analysis frequency features semantically aware features |
| url | https://www.mdpi.com/2078-2489/14/1/34 |
| work_keys_str_mv | AT husammalawadh semanticfeaturesbaseddiscourseanalysisusingdeceptiveandrealtextreviews AT amerahalabrah semanticfeaturesbaseddiscourseanalysisusingdeceptiveandrealtextreviews AT talhameraj semanticfeaturesbaseddiscourseanalysisusingdeceptiveandrealtextreviews AT hafiztayyabrauf semanticfeaturesbaseddiscourseanalysisusingdeceptiveandrealtextreviews |