Can Conversations on Reddit Forecast Future Economic Uncertainty? An Interpretable Machine Learning Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: Jiang, Jinhang; Feng, Mei; Srinivasan, Karthik

Abstract: In recent years, social media has become an indispensable source of information ... Expand

Abstract: In recent years, social media has become an indispensable source of information through which public attitudes, opinions, and concerns can be studied and quantified. This paper proposes an interpretable machine learning framework for predicting the Equity Market-related Economic Uncertainty Index using features generated from a popular discussion forum on Reddit. Our framework consists of a series of custom preprocessing and analytics methods, including BERTopic for latent topic identification and regularized linear models. Using our framework, we evaluate explanatory models with different configurations over a large corpus of Reddit posts belonging to the personal finance category. Our analysis generates valuable insights about discussion topics on Reddit and their efficacy in accurately predicting future economic uncertainty. The study demonstrates the potential of using social media data and interpretable machine learning to inform economic forecasting research. Collapse

Topics: Reddit social media explainable artificial intelligence Python permutation feature importance

Methods: descriptive statistic time series analysis longitudinal research topic model experiment

Facilitators and Barriers of Artificial Intelligence Adoption in Business – Insights from Opinions Using Big Data Analytics

2023 | Information Systems Frontiers | Citations: 0

Authors: Kar, Arpan Kumar; Kushwaha, Amit Kumar

Abstract: Data-driven predictions have become an inseparable part of business decisions. A ... Expand

Abstract: Data-driven predictions have become an inseparable part of business decisions. Artificial Intelligence (AI) has started helping the product and support teams perform more accurate experiments in various business settings. This study proposes a framework for businesses based on inductive learnings related to success and barriers shared on social media platforms. Our goal is to analyse the signals emerging from these conversational opinions from the early adoption of AI, with a focus towards facilitators and barriers faced by teams. Factors like efficiency, innovation, business research, product novelty, manual intervention, adaptability, emotion, support, personal growth, experiential learning, fear of failure and fear of upgradation have been identified based on an exploratory study and then a confirmatory study. We present the learnings through a roadmap for practitioners. This study contributes to the IS literature by delineating AI as a determinant of success and introduces a lot of organizational factors into the model. Collapse

Topics: artificial intelligence Twitter innovation management big data social media

Methods: topic model literature study synthesis regression analysis method data transformation

Theories: organizational theory organizational behavior theory management theory technology acceptance model organizational learning theory

Finding a Needle in the Haystack: Recommending Online Communities on Social Media Platforms Using Network and Design Science

2021 | Journal of the Association for Information Systems | Citations: 0

Authors: Velichety, Srikar; Ram, Sudha

Abstract: We address the problem of recommending online communities on social media platf ... Expand

Abstract: We address the problem of recommending online communities on social media platforms using design science. Our method is grounded in network science and leverages the random surfer model of the web, small-world networks, strength of weak connections, and connectivity to analyze three types of large-scale networks. In doing so, we design features for structural hole assortativity and local clustering coefficient rank to capture both the diversity and evolution of user interests. We also extract general online community features such as size and overlap. Experiments conducted on a large dataset of 34,000 lists created and subscribed to by 1,600 active Twitter users over a six-month period showed that our network features outperform the general and content features in terms of recommending communities at the top position. In addition, a combination of general and network features generated the best results in the top position with a significant performance improvement over using only the content features. A combination of all three types of features gave the best results in the top-5 and top-10 positions while improving the quality of recommendations at every other position. Our work outperforms the latest work on community recommendations on social media platforms and has major implications for the design of online community recommenders. Collapse

Topics: Twitter social media online community recommender system big data

Methods: computational algorithm design science cluster analysis artificial neural network deep learning

Enhancing Social Media Analysis with Visual Data Analytics: A Deep Learning Approach

2020 | Management Information Systems Quarterly | Citations: 0

Authors: Donghyuk Shin; Shu He; Gene Moo Lee; Whinston, Andrew B.; Cetintas, Suleyman; Kuang-Chih Lee

Abstract: This research methods article proposes a visual data analytics framework to enha ... Expand

Abstract: This research methods article proposes a visual data analytics framework to enhance social media research using deep learning models. Drawing on the literature of information systems and marketing, complemented with data-driven methods, we propose a number of visual and textual content features including complexity, similarity, and consistency measures that can play important roles in the persuasiveness of social media content. We then employ state-of-the-art machine learning approaches such as deep learning and text mining to operationalize these new content features in a scalable and systematic manner. For the newly developed features, we validate them against human coders on Amazon Mechanical Turk. Furthermore, we conduct two case studies with a large social media dataset from Tumblr to show the effectiveness of the proposed content features. The first case study demonstrates that both theoretically motivated and data-driven features significantly improve the model’s power to predict the popularity of a post, and the second one highlights the relationships between content features and consumer evaluations of the corresponding posts. The proposed research framework illustrates how deep learning methods can enhance the analysis of unstructured visual and textual data for social media research. Collapse

Topics: social media Tumblr blog analytical information system Flickr

Methods: deep learning case study word2vec machine learning experiment

Theories: elaboration likelihood model

Matching Mobile Applications for Cross-Promotion

2020 | Information Systems Research | Citations: 0

Authors: Lee, Gene Moo; He, Shu; Lee, Joowon; Whinston, Andrew B.

Abstract: The mobile applications (apps) market is one of the most successful software mar ... Expand

Abstract: The mobile applications (apps) market is one of the most successful software markets. As the platform grows rapidly, with millions of apps and billions of users, search costs are increasing tremendously. The challenge is how app developers can target the right users with their apps and how consumers can find the apps that fit their needs. Cross-promotion, advertising a mobile app (target app) in another app (source app), is introduced as a new app-promotion framework to alleviate the issue of search costs. In this paper, we model source app user behaviors (downloads and postdownload usages) with respect to different target apps in cross-promotion campaigns. We construct a novel app similarity measure using latent Dirichlet allocation topic modeling on apps’ production descriptions and then analyze how the similarity between the source and target apps influences users’ app download and usage decisions. To estimate the model, we use a unique data set from a large-scale random matching experiment conducted by a major mobile advertising company in Korea. The empirical results show that consumers prefer more diversified apps when they are making download decisions compared with their usage decisions, which is supported by the psychology literature on people’s variety-seeking behavior. Lastly, we propose an app-matching system based on machine-learning models (on app download and usage prediction) and generalized deferred acceptance algorithms. The simulation results show that app analytics capability is essential in building accurate prediction models and in increasing ad effectiveness of cross-promotion campaigns and that, at the expense of privacy, individual user data can further improve the matching performance. This paper has implications on the trade-off between utility and privacy in the growing mobile economy. Collapse

Topics: mobile application mobile application market digital distribution marketing management privacy

Methods: computational algorithm simulation machine learning topic model experiment

Mining Semantic Soft Factors for Credit Risk Evaluation in Peer-to-Peer Lending

2020 | Journal of Management Information Systems | Citations: 0

Authors: Wang, Zhao; Jiang, Cuiqing; Zhao, Huimin; Ding, Yong

Abstract: While Peer-to-Peer (P2P) lending is rapidly growing, it is also accompanied by h ... Expand

Abstract: While Peer-to-Peer (P2P) lending is rapidly growing, it is also accompanied by high credit risk due to information asymmetry. Besides conventional hard information, soft information also enters into the lending decision process. The descriptive loan texts submitted by borrowers have great potential for exploiting useful soft factors, but also pose great challenges due to the semantic sensitivity to context and the complexity of content representation. We propose a novel text mining method for automatically extracting semantic soft factors from descriptive loan texts. The method maps terms to an embedding space, assembles semantically related terms together into semantic cliques, and then defines semantic soft factors corresponding to the semantic cliques. Empirical evaluation shows that the extracted semantic soft factors contributed to significant improvement on credit risk evaluation in terms of both discrimination performance and granting performance. This work advances our knowledge of soft information indicative of a borrower's credit risk. Collapse

Topics: crowdsourcing peer-to-peer lending information retrieval peer-to-peer model missing data

Methods: machine learning computational algorithm classification method lasso regression natural language processing

Text Mining for Information Systems Researchers: An Annotated Topic Modeling Tutorial

2016 | Communications of the Association for Information Systems | Citations: 127

Authors: Debortoli, Stefan; Müller, Oliver; Junglas, Iris; vom Brocke, Jan

Abstract: Analysts have estimated that more than 80 percent of today’s data is stored in u ... Expand

Abstract: Analysts have estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video)—much of it expressed in rich and ambiguous natural language. Traditionally, to analyze natural language, one has used qualitative data-analysis approaches, such as manual coding. Yet, the size of text data sets obtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challenges encountered when applying automated text-mining techniques in information systems research. In particular, we showcase how to use probabilistic topic modeling via Latent Dirichlet allocation, an unsupervised text-mining technique, with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews. For fellow information systems researchers, this tutorial provides guidance for conducting text-mining studies on their own and for evaluating the quality of others. Collapse

Topics: user satisfaction service quality technology adoption data quality Python

Methods: topic model natural language processing lasso regression multinomial logistic regression latent dirichlet allocation

Theories: technology acceptance model information systems success model information systems theory

ISTopic: Understanding Information Systems Research through Topic Models

2015 | International Conference on Information Systems | Citations: 3

Authors: Chen, Hailiang; Zhao, Leon

Abstract: What are the fundamental research questions in Information Systems? How do vario ... Expand

Abstract: What are the fundamental research questions in Information Systems? How do various research topics relate with one another to form the IS research landscape and how do they evolve over time? This study is an initial attempt to answer these questions using topic models to investigate the topics examined by the premier IS journals in 1977-2014. We present an IS Topic Graph that contains 33 research areas, 31 of which are closely connected with one another. Further analyses of this graph reveal how different IS research areas are intertwined to the extent that they are almost inseparable. Looking into IS research at a finer level, we identify 300 research topics, and a chronological analysis reveals a trend of topic diversification and externalization. To guide future research, an intelligent literature search tool called ISTopic is built and is available at http://www.istopic.org for public access. Collapse

Topics: IS discipline outsourcing website

Methods: topic model literature study latent dirichlet allocation Gibbs sampling lasso regression