publication . Preprint . 2018

ReviewChain: Untampered Product Reviews on the Blockchain

Martens, Daniel; Maalej, Walid;
Open Access English
  • Published: 05 Mar 2018
Abstract
Online portals include an increasing amount of user feedback in form of ratings and reviews. Recent research highlighted the importance of this feedback and confirmed that positive feedback improves product sales figures and thus its success. However, online portals' operators act as central authorities throughout the overall review process. In the worst case, operators can exclude users from submitting reviews, modify existing reviews, and introduce fake reviews by fictional consumers. This paper presents ReviewChain, a decentralized review approach. Our approach avoids central authorities by using blockchain technologies, decentralized apps and storage. Thereb...
Subjects
free text keywords: Computer Science - Computers and Society
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[1] Cuneyt Gurcan Akcora, Yulia R Gel, and Murat Kantarcioglu. 2017. Blockchain - A Graph Primer. CoRR (2017). [OpenAIRE]

[2] Jyrki Alakuijala, Evgenii Kliuchnikov, Zoltan Szabadka, and Lode Vandevenne. 2015. Comparison of brotli, deflate, zopfli, lzma, lzham and bzip2 compression algorithms. Google Inc. (2015).

[3] Patrali Chatterjee. 2001. Online reviews: do consumers use them?. In Association for Consumer Research 2001 Proceedings. 129-134.

[4] Tommaso Fornaciari and Massimo Poesio. 2014. Identifying fake Amazon reviews as learning from crowds. EACL. [OpenAIRE]

[5] Ulrike Gretzel and Kyung Hyan Yoo. 2008. Use and Impact of Online Travel Reviews. (2008), 35-46. https://doi.org/10.1007/978-3-211-77280-5_4 [OpenAIRE]

[6] M Harman, Yue Jia, and Yuanyuan Zhang. 2012. App store mining and analysis: MSR for app stores. In 2012 9th IEEE Working Conference on Mining Software Repositories (MSR 2012). IEEE, 108-111. https://doi.org/10.1109/MSR.2012.6224306

[7] Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In the 2004 ACM SIGKDD international conference. ACM Press, New York, New York, USA, 168-177. https://doi.org/10.1145/1014052.1014073

[8] Nitin Jindal and Bing Liu. 2008. Opinion spam and analysis. In the international conference. ACM Press, New York, New York, USA, 219-230. https://doi.org/10. 1145/1341531.1341560

[9] Susan M Mudambi and David Schuf. 2010. What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly (2010). [OpenAIRE]

[10] Arjun Mukherjee, Vivek Venkataraman, Bing Liu 0001, and Natalie S Glance. 2013. What Yelp Fake Review Filter Might Be Doing? ICWSM.

[11] Dennis Pagano and Walid Maalej. 2013. User feedback in the appstore - An empirical study. RE, 125-134. https://doi.org/10.1109/RE.2013.6636712

[12] Nick Szabo. 1994. Smart contracts. (1994). http://szabo.best.vwh.net/smart. contracts.html

[13] Peter Szilagy. 2017. Go Ethereum: Mobile account management. (2017). https: //github.com/ethereum/go-ethereum/wiki/Mobile:-Account-management

[14] Gavin Wood. 2014. Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper 151. (2014).

Abstract
Online portals include an increasing amount of user feedback in form of ratings and reviews. Recent research highlighted the importance of this feedback and confirmed that positive feedback improves product sales figures and thus its success. However, online portals' operators act as central authorities throughout the overall review process. In the worst case, operators can exclude users from submitting reviews, modify existing reviews, and introduce fake reviews by fictional consumers. This paper presents ReviewChain, a decentralized review approach. Our approach avoids central authorities by using blockchain technologies, decentralized apps and storage. Thereb...
Subjects
free text keywords: Computer Science - Computers and Society
Download from

[1] Cuneyt Gurcan Akcora, Yulia R Gel, and Murat Kantarcioglu. 2017. Blockchain - A Graph Primer. CoRR (2017). [OpenAIRE]

[2] Jyrki Alakuijala, Evgenii Kliuchnikov, Zoltan Szabadka, and Lode Vandevenne. 2015. Comparison of brotli, deflate, zopfli, lzma, lzham and bzip2 compression algorithms. Google Inc. (2015).

[3] Patrali Chatterjee. 2001. Online reviews: do consumers use them?. In Association for Consumer Research 2001 Proceedings. 129-134.

[4] Tommaso Fornaciari and Massimo Poesio. 2014. Identifying fake Amazon reviews as learning from crowds. EACL. [OpenAIRE]

[5] Ulrike Gretzel and Kyung Hyan Yoo. 2008. Use and Impact of Online Travel Reviews. (2008), 35-46. https://doi.org/10.1007/978-3-211-77280-5_4 [OpenAIRE]

[6] M Harman, Yue Jia, and Yuanyuan Zhang. 2012. App store mining and analysis: MSR for app stores. In 2012 9th IEEE Working Conference on Mining Software Repositories (MSR 2012). IEEE, 108-111. https://doi.org/10.1109/MSR.2012.6224306

[7] Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In the 2004 ACM SIGKDD international conference. ACM Press, New York, New York, USA, 168-177. https://doi.org/10.1145/1014052.1014073

[8] Nitin Jindal and Bing Liu. 2008. Opinion spam and analysis. In the international conference. ACM Press, New York, New York, USA, 219-230. https://doi.org/10. 1145/1341531.1341560

[9] Susan M Mudambi and David Schuf. 2010. What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly (2010). [OpenAIRE]

[10] Arjun Mukherjee, Vivek Venkataraman, Bing Liu 0001, and Natalie S Glance. 2013. What Yelp Fake Review Filter Might Be Doing? ICWSM.

[11] Dennis Pagano and Walid Maalej. 2013. User feedback in the appstore - An empirical study. RE, 125-134. https://doi.org/10.1109/RE.2013.6636712

[12] Nick Szabo. 1994. Smart contracts. (1994). http://szabo.best.vwh.net/smart. contracts.html

[13] Peter Szilagy. 2017. Go Ethereum: Mobile account management. (2017). https: //github.com/ethereum/go-ethereum/wiki/Mobile:-Account-management

[14] Gavin Wood. 2014. Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper 151. (2014).

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