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Catching the Viewer's Eye: Examining Exploration and Exploitation Strategies in the Live Streaming Market

2023 | International Conference on Information Systems | Citations: 0
Authors: Kwon, Sung Hyun; Hann, Il-Horn

Abstract: Live streaming has become an important feature on social media platforms like F ... Expand

Abstract: Live streaming has become an important feature on social media platforms like Facebook and Instagram. More and more entrepreneurs (content creators) are competing in live streaming platforms like Twitch to maximize the attention they receive from consumers (viewers). In this competitive landscape, it is crucial for entrepreneurs to develop and provide new compelling content that can maximize the consumers' attention and aid the discovery of their content. We adopt an exploration-exploitation framework and assess the four strategies these new entrepreneurs could use to attract viewership and position themselves on Twitch: exploration, exploitation, learning from viewers, and their neighbor streamers. We combined the natural language processing techniques with theory-driven measures to accomplish this. Using our proposed measures, we estimate the utility of consumers from these different strategies using the discrete choice demand model. Collapse

Semantic filters: DeBERTa

Topics: Twitch database system decision making wiki online game

Methods: natural language processing RoBERTa transformer model BERT language model
Fake Review Detection - The Value of Domain-Specificity

2023 | Americas Conference on Information Systems | Citations: 0
Authors: Theuerkauf, René; Peters, Ralf

Abstract: The issue of fake reviews, a perennial challenge faced by e-commerce companies, ... Expand

Abstract: The issue of fake reviews, a perennial challenge faced by e-commerce companies, has been worsened in recent years. In this paper, we investigate the trade-off between the application of general-purpose and domain-specific model by selecting training data using transformer-based models to identify fake reviews. Therefore, we compare two scenarios using different set ups of data selection. First, a general-purpose model was identified and applied on specific domains. Afterwards, domain-specific models were trained and tested. Then, the results from these scenarios were compared, yielding the conclusion that models trained on data from a similar product category outperform general-purpose models in classification performance up to 21% (on average by 4%). Our findings send an important message to e-commerce companies to rethink their strategy on training general-purpose models to identify fake reviews to create several, more domainspecific models on that task according to present data domains. Collapse

Semantic filters: DeBERTa

Topics: fake review online review Facebook electronic commerce recommender system

Methods: RoBERTa DeBERTa BERT transformer model Cross-Industry Standard Process for Data Mining

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Catching the Viewer's Eye: Examining Exploration and Exploitation Strategies in the Live Streaming Market

Fake Review Detection - The Value of Domain-Specificity