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Fine-grained classification of drug trafficking based on Instagram hashtags
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2023 | Decision Support Systems | Citations: 0
Authors: Hu, Chuanbo; Liu, Bin; Ye, Yanfang; Li, Xin

Abstract: Social networks have become important platforms for the marketing and sale of il ... Expand

Abstract: Social networks have become important platforms for the marketing and sale of illicit drugs. Hashtags make it easier for users to engage in drug trafficking, further increasing the risk of drug abuse. However, there are significant challenges in the detection and management of drug trafficking activities. In addition, the rapid legalization of some drugs has required a fine-grained classification of drugs to distinguish them from those that are illegal. Motivated by these observations, in this paper, our aim is to develop a methodology using the latest advances in AI technology to classify hashtags from posts advertising illicit drugs for sale on social networks. We present a semi-supervised deep learning approach to classify hashtags from posts advertising illicit drugs. An elegant combination of Bidirectional Encoder Representations from Transformers (BERT) with Graph Convolu tional Network (GCN) allows us to analyze the characteristics (e.g., shipping region and platform self-regulation) of illegal drug trafficking. Our BERT+GCN model achieved the best performance with more than 75% accuracy compared to the other three baseline models. Then, fine-grained hashtags identified are applied to explore the characteristics of drug trafficking. Finally, we report our results for further exploration of shipping regions and self-regulation of drug trafficking on the platform in two analysis scenarios. Our developed approach has shown its effectiveness in detecting hashtags for different types of drugs from illegal drug sellers. Based on hashtag classification, we also provide two case studies that indicate that (1) there are differences in self-regulation for different types of drugs on social media, (2) there are regional differences in the demand for different types of drugs. Collapse

Semantic filters: generative pre-trained transformer 2

Topics: Instagram social media Google+ artificial intelligence advertising management

Methods: BERT case study machine learning deep learning semi-supervised learning
Social Media and Fake News Detection using Adversarial Collaboration
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2022 | HICSS | Citations: 0
Authors: Dsouza, Karen; French, Aaron

Abstract: The diffusion of fake information on social media networks obscures public perce ... Expand

Abstract: The diffusion of fake information on social media networks obscures public perception of events, news, and relevant content. Intentional misleading news may promote negative online experiences and influence societal behavioral changes such as increased anxiety, loneliness, and inadequacy. Adversarial attacks target creating misinformation in online information systems. This behavior can be viewed as an instrument to manipulate the online social media networks for cultural, social, economic, and political gains. A method to test a deep learning model- long short-term memory (LSTM) using adversarial examples generated from a transformer model has been presented. The paper attempts to examine features in machine learning algorithms that propagate fake news. Another goal is to evaluate and compare the usefulness of generative adversarial networks with long-term short-term recurrent neural network algorithms in identifying fake news. A closer look at the mechanisms of implementing adversarial attacks in social media systems helps build robust intelligent systems that can withstand future vulnerabilities. Collapse

Semantic filters: generative pre-trained transformer 2

Topics: fake news social media Python database system web page

Methods: computational algorithm experiment machine learning deep learning artificial neural network
How to Achieve Goals in Digital Games: An Empirical Test of a Goal-Oriented Model in Pokemon GO
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2022 | Journal of the Association for Information Systems | Citations: 0
Authors: Kwak, Dong-Heon; Deng, Shuyuan; Kuem, Jungwon; Kim, Sung

Abstract: To effectively design digital games and gamified systems, it is important to pr ... Expand

Abstract: To effectively design digital games and gamified systems, it is important to properly understand the psychological and behavioral processes that players use to reach goals. Although numerous prior studies have examined individuals' adoption, use, and continued use of digital games, few attempts have been made to understand how people desire and strive to achieve goals. The objective of this study is to develop and test a model of individuals' achievement of goals in digital gaming. Drawing upon theories of goal-directed behavior, we propose a conceptual model describing goal setting, goal striving, goal attainment, and feedback evaluations in the context of mobile gaming. To empirically test the proposed model, we collected two sets of (cross-sectional and longitudinal) data from 407 users of Pokémon GO. The results generally indicate that goal-directed effort plays an important role in translating goal desire into goal attainment. In addition, we found prior game points and goal desire have interaction effects on goal-directed effort and the subsequent acquisition of game points. Finally, this study shows that action strategies such as in-game payment and deliberate planning have differential effects on goal-directed effort and satisfying experiences. Overall, our findings provide empirical support for the efficacy of our goal-oriented model as a theoretical tool for explaining the process of goal striving to obtain game points. Our findings not only have important implications for digital gaming but also contribute to emerging research on gamified systems. Collapse

Semantic filters: generative pre-trained transformer 2

Topics: video game mobile game mobile system intrinsic motivation mobile application

Methods: survey business process modeling longitudinal research conceptual modelling psychometrics
From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research
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2021 | European Journal of Information Systems | Citations: 0
Authors: Li, Yan; Thomas, Manoj a; Liu, Dapeng

Abstract: Natural Language Processing (NLP) is now widely integrated into web and mobile a ... Expand

Abstract: Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between humans and computers. Although there is a large body of NLP studies published in Information Systems (IS), a comprehensive review of how NLP research is conceptualised and realised in the context of IS has not been conducted. To assess the current state of NLP research in IS, we use a variety of techniques to analyse a literature corpus comprising 356 NLP research articles published in IS journals between 2004 and 2018. Our analysis indicates the need to move from semantics to pragmatics. More importantly, our findings unpack the challenges and assumptions underlying current research trends in NLP. We argue that overcoming these challenges will require a renewed disciplinary IS focus. By proposing a roadmap of NLP research in IS, we draw attention to three NLP research perspectives and present future directions that IS researchers are uniquely positioned to address. Collapse

Semantic filters: generative pre-trained transformer 2

Topics: database system knowledge representation social media analytics social media information retrieval

Methods: natural language processing qualitative content analysis literature study computational algorithm literature sample

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Found 4 articles

Fine-grained classification of drug trafficking based on Instagram hashtags

Social Media and Fake News Detection using Adversarial Collaboration

How to Achieve Goals in Digital Games: An Empirical Test of a Goal-Oriented Model in Pokemon GO

From semantics to pragmatics: where IS can lead in Natural Language Processing (NLP) research