DrugExBERT for Pharmacovigilance – A Novel Approach for Detecting Drug Experiences from User-Generated Content

2023 | International Conference on Information Systems | Citations: 0

Authors: Bohnen, Eva; Erlebach, Stefanie; Zimmermann, Steffen

Abstract: Pharmaceutical companies have to maintain drug safety through pharmacovigilance ... Expand

Abstract: Pharmaceutical companies have to maintain drug safety through pharmacovigilance systems by monitoring various sources of information about adverse drug experiences. Recently, user-generated content (UGC) has emerged as a valuable source of real-world drug experiences, posing new challenges due to its high volume and variety. We present DrugExBERT, a novel approach to extract adverse drug experiences (adverse reaction, lack of effect) and supportive drug experiences (effectiveness, intervention, indication, and off-label use) from UGC. To be able to verify the extracted drug experiences, DrugExBERT additionally provides explications in the form of UGC phrases that were critical for the extraction. In our evaluation, we demonstrate that DrugExBERT outperforms state-of-the-art pharmacovigilance approaches as well as ChatGPT on several performance measures and that DrugExBERT is data-and drug-agnostic. Thus, our novel approach can help pharmaceutical companies meet their legal obligations and ethical responsibility while ensuring patient safety and monitoring drug effectiveness. Collapse

Topics: health information system application programming interface generative AI database system

Methods: chatGPT natural language processing RoBERTa BERT machine learning

Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

2023 | International Journal of Information Management | Citations: 0

Authors: Dwivedi, Yogesh K.; Kshetri, Nir; Hughes, Laurie; Slade, Emma Louise; Jeyaraj, Anand; Kar, Arpan Kumar; Baabdullah, Abdullah M.; Koohang, Alex; Raghavan, Vishnupriya; Ahuja, Manju; Albanna, Hanaa; Albashrawi, Mousa Ahmad; Al-Busaidi, Adil S.; Balakrishnan, Janarthanan; Barlette, Yves; Basu, Sriparna; Bose, Indranil; Brooks, Laurence; Buhalis, Dimitrios; Carter, Lemuria; Chowdhury, Soumyadeb; Crick, Tom; Cunningham, Scott W.; Davies, Gareth H.; Davison, Robert M.; Dé, Rahul; Dennehy, Denis; Duan, Yanqing; Dubey, Rameshwar; Dwivedi, Rohita; Edwards, John S.; Flavián, Carlos; Gauld, Robin; Grover, Varun; Hu, Mei-Chih; Janssen, Marijn; Jones, Paul; Junglas, Iris; Khorana, Sangeeta; Kraus, Sascha; Larsen, Kai R.; Latreille, Paul; Laumer, Sven; Malik, F. Tegwen; Mardani, Abbas; Mariani, Marcello; Mithas, Sunil; Mogaji, Emmanuel; Nord, Jeretta Horn; O’Connor, Siobhan; Okumus, Fevzi; Pagani, Margherita; Pandey, Neeraj; Papagiannidis, Savvas; Pappas, Ilias O.; Pathak, Nishith; Pries-Heje, Jan; Raman, Ramakrishnan; Rana, Nripendra P.; Rehm, Sven-Volker; Ribeiro-Navarrete, Samuel; Richter, Alexander; Rowe, Frantz; Sarker, Suprateek; Stahl, Bernd Carsten; Tiwari, Manoj Kumar; Van Der Aalst, Wil; Venkatesh, Viswanath; Viglia, Giampaolo; Wade, Michael; Walton, Paul; Wirtz, Jochen; Wright, Ryan

Abstract: ☆ This editorial opinion paper provides a subjective viewpoint on the potential ... Expand

Abstract: ☆ This editorial opinion paper provides a subjective viewpoint on the potential impact of generative AI technologies such as ChatGPT in the domains of education, business, and society. Its objective is to offer initial guidance on the opportunities, challenges, and implications associated with these technologies. It is worth noting that, given its nature as an editorial opinion piece, this submission has not undergone a formal double-blind review process but has been reviewed informally by appropriate experts.Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT's capabilities to enhance productivity and suggest that it is likely to offer 1 Equal contributions Y.K. Dwivedi et al.International Journal of Information Management 71 (2023) 102642 3 significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT's use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts. Collapse

Topics: artificial intelligence generative AI Twitter privacy innovation management

Methods: chatGPT chatbot generative pre-trained transformer literature study language model

Taking the Person Seriously: Ethically Aware IS Research in the Era of Reinforcement Learning-Based Personalization

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Greene, Travis; Shmueli, Galit; Ray, Soumya

Abstract: Advances in reinforcement learning and implicit data collection on large-scale ... Expand

Abstract: Advances in reinforcement learning and implicit data collection on large-scale commercial platforms mark the beginning of a new era of personalization aimed at the adaptive control of human user environments. We present five emergent features of this new paradigm of personalization that endanger persons and societies at scale and analyze their potential to reduce personal autonomy, destabilize social and political systems, and facilitate mass surveillance and social control, among other concerns. We argue that current data protection laws, most notably the European Union's General Data Protection Regulation, are limited in their ability to adequately address many of these issues. Nevertheless, we believe that IS researchers are well-situated to engage with and investigate this new era of personalization. We propose three distinct directions for ethically aware reinforcement learning-based personalization research uniquely suited to the strengths of IS researchers across the sociotechnical spectrum. Collapse

Topics: usability personalization legal context General Data Protection Regulation information privacy law

Methods: reinforcement learning computational algorithm simulation language model experiment

Theories: social sciences theory

ChatGPT and GPTZero in Research and Social Media: A Sentiment- and Topic-based Analysis

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Heumann, Maximilian; Kraschewski, Tobias; Breitner, Michael H.

Abstract: Conversational AI agents like ChatGPT have gained considerable attention in rec ... Expand

Abstract: Conversational AI agents like ChatGPT have gained considerable attention in recent years, with potential implications across various domains. However, there is a gap in understanding the prevailing opinions, topics, and concerns of the public and research communities regarding these technologies. This study conducts a semi-automated analysis of over 1,006,000 tweets and 279 scholarly articles to explore perspectives on ChatGPT and its potential misuse for plagiarism, as well as the comparatively lower interest in GPTZero, a technology designed to counter AI-driven plagiarism. Employing advanced natural language processing techniques, such as sentiment analysis and topic modeling, we identify challenges and opportunities related to ChatGPT and GPTZero. Our findings uncover potential areas for improvement and provide guidance for future research and development in the field of conversational AI. Collapse

Topics: Twitter artificial intelligence human computer interaction usability Python

Methods: chatGPT topic model chatbot sentiment analysis natural language processing

Are Deep Learning-Generated Social Media Profiles Indistinguishable from Real Profiles?

2023 | HICSS | Citations: 0

Authors: Rossi, S.; Kwon, Y.; Auglend, O.; Mukkamala, R.; Rossi, M.; Thatcher, J.

Abstract: In recent years, deep learning methods have become increasingly capable of gene ... Expand

Abstract: In recent years, deep learning methods have become increasingly capable of generating near photorealistic pictures and humanlike text up to the point that humans can no longer recognize what is realand what is AI-generated. Concerningly, there is evidence that some of these methods have already been adopted to produce fake social media profiles and content. We hypothesize that these advances have made detecting generated fake social media content in the feed extremely difficult, if not impossible, for the average user of social media. This paper presents the results of an experiment where 375 participants attempted to label real and generated profiles and posts in a simulated social media feed. The results support our hypothesis and suggest that even fully-generated fake profiles with posts written by an advanced text generator are difficult for humans to identify. Collapse

Topics: social media Twitter social network armed conflict website

Methods: experiment deep learning survey descriptive statistic stochastic model

Explainable and responsible artificial intelligence

2022 | Electronic Markets | Citations: 4

Authors: Meske, Christian; Abedin, Babak; Klier, Mathias; Rabhi, Fethi

Abstract: The special issueWith a focus on the transformation of electronic markets, in t ... Expand

Abstract: The special issueWith a focus on the transformation of electronic markets, in this special issue, we explore and extend research on how to establish explainability and responsibility in intelligent black box systems-machine learning-based or not. The submitted Collapse

Topics: explainable artificial intelligence artificial intelligence electronic market electronic commerce business model

Methods: decision tree classification natural language processing experiment BERT machine learning

A Weakly Supervised WordNet-Guided Deep Learning Approach to Extracting Aspect Terms from Online Reviews

2020 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Tao, Jie; Zhou, Lina

Abstract: The unstructured nature of online reviews makes it inefficient and inconvenient ... Expand

Abstract: The unstructured nature of online reviews makes it inefficient and inconvenient for prospective consumers to research and use in support of purchase decision making. The aspects of products provide a fine-grained meaningful perspective for understanding and organizing review texts. Traditional aspect term extraction approaches rely on discrete language models that treat words in isolation. Despite that continuous-space language models have demonstrated promise in addressing a wide range of problems, their application in aspect term extraction faces significant challenges. For instance, existing continuous-space language models typically require large collections of labeled data, which remain difficult to obtain in many domains. More importantly, previous methods are largely data driven but overlook the role of human knowledge in guiding model development. To address these limitations, this study designs and develops weakly supervised WordNet-guided deep learning to aspect term extraction. The approach draws on deep-level semantic information from WordNet to guide not only the selection representative seed terms but also the pruning of aspect candidate terms. The weak supervision is provided by a very small set of labeled data. We conduct a comprehensive evaluation of the proposed method using both direct and indirect methods. The evaluation results with Yelp restaurant reviews demonstrate that our proposed method consistently outperforms all baseline methods including discrete models and the state-of-the-art continuous-space language models for aspect term extraction across both direct and indirect evaluations. The research findings have broad research, technical, and practical implications for various stakeholders of online reviews. Collapse

Topics: knowledge representation online review organizational productivity user-generated content decision making

Methods: language model WordNet structural equation modeling word embedding deep learning

Brute - Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection

2019 | Journal of the Association for Information Systems | Citations: 0

Authors: Ptaszynski, Michal; Lempa, Pawel; Masui, Fumito; Kimura, Yasutomo; Rzepka, Rafal; Araki, Kenji; Wroczynski, Michal; Leliwa, Gniewosz

Abstract: Cyberbullying, or humiliating people using the Internet, has existed almost sinc ... Expand

Abstract: Cyberbullying, or humiliating people using the Internet, has existed almost since the beginning of Internet communication. The relatively recent introduction of smartphones and tablet computers has caused cyberbullying to evolve into a serious social problem. In Japan, members of a parent-teacher association (PTA) attempted to address the problem by scanning the Internet for cyberbullying entries. To help these PTA members and other interested parties confront this difficult task we propose a novel method for automatic detection of malicious Internet content. This method is based on a combinatorial approach resembling brute-force search algorithms, but applied in language classification. The method extracts sophisticated patterns from sentences and uses them in classification. The experiments performed on actual cyberbullying data reveal an advantage of our method vis-à-vis previous methods. Next, we implemented the method into an application for Android smartphones to automatically detect possible harmful content in messages. The method performed well in the Android environment, but still needs to be optimized for time efficiency in order to be used in practice. Collapse

Topics: cyberbullying internet technology Android smartphone mobile system

Methods: machine learning experiment support vector machine computational algorithm parametric test