An Investigation of Domain-based Social Influence on ChatGPT-Related Twitter Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Ahangama, Supunmali

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Topics: Twitter social influence social media social identity robotic

Methods: chatGPT sentiment analysis multiple linear regression term frequency–inverse document frequency NLTK

sPAN-pROP: Combatting Contextualized Social Media State-Linked Propaganda in the Middle East

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Aleroud, Ahmed; Melhem, Abdullah Bani; Alhussien, Nour; Albert, Craig

Abstract: Social Cyber-attacks such as propaganda, conspiracy theories, anger, and hate d ... Expand

Abstract: Social Cyber-attacks such as propaganda, conspiracy theories, anger, and hate discourse are very old phenomena that inflict harm to humans, organizations, national security, public officials, democratization efforts, careers, and policies. There have been significant efforts to identify anti-US speech on social media which includes propaganda. Such efforts largely ignore the attempts by other countries to manipulate social media in some regions including the Middle East. Research in this area is computational and solely focuses on fine tuning language models to detect general propaganda attacks. This paper addresses a new category of propaganda attacks that are tied to state-linked accounts that spread anti-US propaganda by taking advantages of specific geo-political crises in the Middle East. We investigated the role of general language models and training data to detect those forms of propaganda. Our study concludes that existing propaganda training data is unable to successfully detect targeted propaganda. We propose a contextualized span detection approach to identify these types of propaganda and show that our targeted training models work significantly better compared to the existing general propaganda detection approaches. Collapse

Topics: Twitter IT security threat social media armed conflict misinformation

Methods: language model experiment deep learning PyTorch literature study

Design of a Novel Information System for Semi-automated Management of Cybersecurity in Industrial Control Systems

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Ameri, Kimia; Hempel, Michael; Sharif, Hamid; Lopez, Juan; Perumalla, Kalyan

Abstract: There is an urgent need in many critical infrastructure sectors, including the e ... Expand

Abstract: There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this article presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. We developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports. Collapse

Topics: IT security database system website Python electronic authentication

Methods: computational algorithm sentiment analysis natural language processing BERT language model

Great Divisions: The Evolution of Polarization During the Man-made Emergency of January 6, 2021

2023 | HICSS | Citations: 0

Authors: Bhatt, P.; Vemprala, N.; Valecha, R.; Hariharan, G.; Rao, H.

Abstract: Polarization, which refers to the formation of opposing groups based on peoples ... Expand

Abstract: Polarization, which refers to the formation of opposing groups based on peoples' beliefs and opinions, is a phenomenon that has existed for eons. However, in the recent past, social media has caused a quantum change in the dynamics of polarization since beliefs may change almost instantaneously on social media because of unfolding events. We investigate social media communication that has resulted in polarized opinions among individuals prior to, during, and after a crisis event; the January 6th US Capitol riots. Analyses of the dominant narratives on Twitter surrounding the incident reveal a high level of polarization throughout the unfolding of the event, with increased polarization possibly attributable to the onset of the crisis. We also observe the evolution of the phenomenon: as the event unfolds, polarization changes. We suggest that understanding the evolution of polarization is important for timely crisis resolution by crisis managers. We utilize three measures, spread, distinctness and regionalization, to examine polarization during a crisis. Based on these measures our findings indicate that social media discussions exhibit an increase in the negative sentiment during the crisis as compared to precrisis, but there is a rebound in terms of positive sentiments emerging in the post crisis period. Also, the results signal reduced diversity in discussion topics during the crisis as compared to pre and post-crisis. Collapse

Topics: social media Twitter terrorist incident artificial intelligence application programming interface

Methods: topic model longitudinal research qualitative content analysis longitudinal survey cluster analysis

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

Augmenting Social Bot Detection with Crowd-Generated Labels

2023 | Information Systems Research | Citations: 0

Authors: Benjamin, Victor; Raghu, T. S.

Abstract: Social media platforms are facing increasing numbers of cyber-adversaries seekin ... Expand

Abstract: Social media platforms are facing increasing numbers of cyber-adversaries seeking to manipulate online discourse by using social bots (i.e., social media software robots) to help automate and scale their attacks. Likewise, some social media users can identify social bot activity at varying degrees of confidence. In this research, human reactions to social bot messages are used to augment existing social bot detection capabilities. Speech act theory is used to inspire a framework for assessing the credibility of instances where users identify potential bot activity, as not all user responses are of equal credibility for assisting with the bot detection task. The framework is then operationalized through deep learning methodologies to develop a computational system for identifying social bots. Real-world performance and practicality of the developed framework is demonstrated on a live, crowd-sourced data set collected from a real-world social media platform. Results show that consideration of crowd reactions to suspected bots can significantly improve bot detection performance. Furthermore, consideration of speech acts to evaluate crowd reactions can even further augment the system’s performance, although speech acts themselves are not necessary to observe performance boost through crowd intelligence. This study serves as a grounding point for future work that can explore an augmented model for detecting other forms of algorithmically generated content within social media platforms.History: Olivia Sheng, Senior Editor; Jesse Bockstedt, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1136. Collapse

Topics: Reddit social media crowdsourcing IT productivity social media analytics

Methods: experiment BERT machine learning support vector machine random forest classification

Theories: speech act theory

Divergent Innovation: Directing the Wisdom of Crowd to Tackle Societal Challenges

2023 | International Conference on Information Systems | Citations: 0

Authors: Cao, Chuhan; Zhu, Jiantao; Xiong, Bingqing; Lim, Eric; Liu, Hefu; Cai, Zhao; Tan, Chee-Wee

Abstract: Crowdsourcing is acknowledged as a promising avenue for addressing societal cha ... Expand

Abstract: Crowdsourcing is acknowledged as a promising avenue for addressing societal challenges by drawing on the wisdom of the crowd to offer diverse solutions to complex problems. Advancing a new conceptual framework of 'divergent innovation' which delineates between topic and quality divergence as focal metrics of performance when crowdsourcing for solutions to societal challenges, this study investigates the impacts of four ideation stimuli on divergent innovation. These four stimuli include task description concreteness, resource richness, topic entropy, and judging criteria comprehensiveness. Empirical analysis based on data sourced from an online crowd-ideation platform reveals that task description concreteness negatively affects topic divergence but positively influences quality divergence, whereas resource richness positively affects topic divergence but negatively influences quality divergence. Additionally, the relationship between topic entropy and topic divergence is U-shaped, with no significant impact on quality divergence. These findings contribute to extant literature on crowdsourcing and offer invaluable insights for practitioners. Collapse

Topics: innovation management societal challenge crowdsourcing evaluation criteria media richness

Methods: BERT literature study statistical hypothesis test regression analysis method language model

A Theory-Driven Deep Learning Method for Voice Chat–Based Customer Response Prediction

2023 | Information Systems Research | Citations: 0

Authors: Chen, Gang; Xiao, Shuaiyong; Zhang, Chenghong; Zhao, Huimin

Abstract: As artificial intelligence and digitalization technologies are flourishing real- ... Expand

Abstract: As artificial intelligence and digitalization technologies are flourishing real-time, online interaction–based commercial modes, exploiting customers’ purchase intention implied in online interaction processes may foster huge business opportunities. In this study, we target the task of voice chat–based customer response prediction in an emerging online interaction–based commercial mode, the invite-online-and-experience-in-store mode. Prior research shows that satisfaction, which can be revealed by the discrepancy between prior expectation and actual experience, is a key factor to disentangle customers’ purchase intention, whereas black-box deep learning methods empirically promise us advantageous capabilities in dealing with complex voice data, for example, text and audio information incorporated in voice chat. To this end, we propose a theory-driven deep learning method that enables us to (1) learn customers’ personalized product preferences and dynamic satisfaction in the absence of their profile information, (2) model customers’ actual experiences based on multiview voice chat information in an interlaced way, and (3) enhance the customer response prediction performance of a black-box deep learning model with theory-driven dynamic satisfaction. Empirical evaluation results demonstrate the advantageous prediction performance of our proposed method over state-of-the-art deep learning alternatives. Investigation of cumulative satisfaction reveals the collaborative predictive roles of theory-driven dynamic satisfaction and deep representation features for customer response prediction. Explanatory analysis further renders insights into customers’ personalized preferences and dynamic satisfaction for key product attributes. History: Yong Tan, Senior Editor; Jingjing Zhang, Associate Editor. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971067 and 72271059] and the China Postdoctoral Science Foundation [Grant 2022M722394]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1196. Collapse

Topics: customer satisfaction willingness to pay decision making marketing information system customer service

Methods: deep learning experiment machine learning BERT theory development

Token Incentives in a Volatile Crypto Market: The Effects of Token Price Volatility on User Contribution

2023 | Journal of Management Information Systems | Citations: 0

Authors: Chen, Kun; Fan, Yifan; Liao, Shaoyi Stephen

Abstract: Crypto tokens, issued and managed via smart contracts, function as rewards in bl ... Expand

Abstract: Crypto tokens, issued and managed via smart contracts, function as rewards in blockchain systems to encourage user participation. Distinct from monetary incentives, token incentives are uncertain in reward magnitude due to the large swings in token prices on crypto markets. By focusing on token price volatility, this study investigates how the reward uncertainty affects user contribution in a tokenized digital platform. Our empirical setting is Steemit, a platform where bloggers write posts and share token rewards based on their posts' popularity. We find that while high token price volatility induces a large volume of blog posts, it diminishes post quality. The dichotomous effects are explained via two mechanisms: users' direct reactions to reward uncertainties and their indirect reactions, mediated by altered token preference amid volatility shocks. Deeply exploring this dynamic process, our results reveal that token price volatility facilitates a platform's network short-term effect but impairs long-term user creativity. Our empirical findings thus extend the literature on blockchain economics and cryptocurrencies and have practical implications for the design of incentive mechanisms on tokenized digital platforms. Collapse

Topics: blog blockchain digital platform cryptocurrency user behavior

Methods: longitudinal research instrumental variables estimation data transformation principal component analysis vector autoregression

Theories: motivation crowding theory

Understanding Emojis for Financial Sentiment Analysis

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Siyi; Xing, Frank

Abstract: ... Expand

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Topics: application programming interface social media website cryptocurrency Python

Methods: sentiment analysis machine learning BERT deep learning experiment