"Do not deceive me anymore!" interpretation through model design and visualization for instagram counterfeit seller account detection. (December 2022)
- Record Type:
- Journal Article
- Title:
- "Do not deceive me anymore!" interpretation through model design and visualization for instagram counterfeit seller account detection. (December 2022)
- Main Title:
- "Do not deceive me anymore!" interpretation through model design and visualization for instagram counterfeit seller account detection
- Authors:
- Park, Jeongeun
Gu, Jinmo
Kim, Ha Young - Abstract:
- Abstract: Counterfeit goods sold online have amounted to $330 billion in losses worldwide. Of these, luxury brand losses account for $30.3 billion. The social and economic effects of counterfeit goods have become more severe with the shift of counterfeit sales channels to social networking services (SNSs) since the first decade of the 2000s when these networks became active. In particular, the number of counterfeit sellers on Instagram increased by more than 171% in 2019. This study endeavors to detect counterfeit seller accounts in the SNS environment through a deep learning method that implements an algorithm to differentiate legitimate and counterfeit seller accounts. We designed a model to classify counterfeit seller accounts and general accounts using image and textual data collected from Instagram. As a result, the proposed model obtained a final account detection accuracy of 100% and demonstrated the possibility of use as a counterfeit seller account detector. Moreover, the elements that influenced the results and the parts that the model focused on were identified using a visual analysis method to improve the interpretation capability of the algorithm and explain the results. The difference between human and machine judgments was analyzed based on the visualization. Highlights: Proposed a deep learning model to detect counterfeit seller accounts on Instagram. Image and text classifiers can be used individually according to the purpose. A visual analysis method isAbstract: Counterfeit goods sold online have amounted to $330 billion in losses worldwide. Of these, luxury brand losses account for $30.3 billion. The social and economic effects of counterfeit goods have become more severe with the shift of counterfeit sales channels to social networking services (SNSs) since the first decade of the 2000s when these networks became active. In particular, the number of counterfeit sellers on Instagram increased by more than 171% in 2019. This study endeavors to detect counterfeit seller accounts in the SNS environment through a deep learning method that implements an algorithm to differentiate legitimate and counterfeit seller accounts. We designed a model to classify counterfeit seller accounts and general accounts using image and textual data collected from Instagram. As a result, the proposed model obtained a final account detection accuracy of 100% and demonstrated the possibility of use as a counterfeit seller account detector. Moreover, the elements that influenced the results and the parts that the model focused on were identified using a visual analysis method to improve the interpretation capability of the algorithm and explain the results. The difference between human and machine judgments was analyzed based on the visualization. Highlights: Proposed a deep learning model to detect counterfeit seller accounts on Instagram. Image and text classifiers can be used individually according to the purpose. A visual analysis method is used to increase the interpretability of the model. Human and machine judgments are compared to distinguish counterfeit seller accounts. … (more)
- Is Part Of:
- Computers in human behavior. Volume 137(2022)
- Journal:
- Computers in human behavior
- Issue:
- Volume 137(2022)
- Issue Display:
- Volume 137, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 137
- Issue:
- 2022
- Issue Sort Value:
- 2022-0137-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-12
- Subjects:
- Deep learning -- User experience -- Instagram -- Social problem -- Multimodal ensemble model -- Explainable artificial intelligence
Interactive computer systems -- Periodicals
Man-machine systems -- Periodicals
004.019 - Journal URLs:
- http://www.sciencedirect.com/science/journal/07475632 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.chb.2022.107418 ↗
- Languages:
- English
- ISSNs:
- 0747-5632
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.921600
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 23050.xml