Are You What You Tweet? The Impact of Sentiment on Digital News Consumption and Social Media Sharing

2023 | Information Systems Research | Citations: 0

Authors: Oh, Hyelim; Goh, Khim-Yong; Phan, Tuan Q.

Abstract: Although social media has helped online newspapers by allowing users to organica ... Expand

Abstract: Although social media has helped online newspapers by allowing users to organically share articles, some have argued that it has cannibalized and hurt newspapers through reduced readership and diminished agenda-setting power. Motivated by these two opposing effects, it is critical to understand what affects the duality between sharing news articles on social media and reading the articles on news websites. Using rich clickstream data on online news readership obtained from an English-language newspaper in an Asian country and social media data collected from Twitter, we focus on article sentiment as a key news content attribute and find a differential effect of sentiment on readership and sharing behaviors across the news site and third-party social media platform. Our results show that people are likely to read news articles with negative sentiment on the news site, but they tend to share articles with positive sentiment on Twitter. Specifically, a one-unit increase in content sentiment is associated with a 10.86% or 273-unit decrease in news site page views but a 17.0% or 2.10-unit increase in Twitter sharing volume. Upon decomposition of news article sentiment, we also find a contrasting positive author sentiment effect and a negative news topic valence effect on news readership. To uncover the underlying mechanism of the findings, we test the key intuitions from prior self-presentation literature. We find that an increase in a Twitter user’s followers (i.e., audience size) leads to an increase in the Twitter user’s propensity to share positive-sentiment news articles. Our findings on the role of sentiment on content consumption and sharing affirm the coopetitive but complementary relationship between news websites and social media platforms. Our results also guide publishers to better craft their news content and manage social media presence to improve audience engagement and readership outcomes while preserving the agenda-setting ability of news media.History: Anindya Ghose, Senior Editor; Mingfeng Lin, Associate Editor.Funding: H. Oh was supported by the Ministry of Education, Singapore [Grant R-253-000-126-114] and the National Research Foundation of Korea [Grant NRF-2021S1A5A8070021]. T. Q. Phan was supported by the National University of Singapore [Grant T1-251RES1402] and the University of Hong Kong [Grant 201909185043].Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1112. Collapse

Topics: Twitter social media website personality Facebook

Methods: sentiment analysis CoreNLP natural language processing instrumental variables estimation econometric modeling

Mapping the Emotional Journey of STD patients in Online Forums

2023 | Americas Conference on Information Systems | Citations: 0

Authors: THIMMANAYAKANAPALYA, Sagarika Suresh; Sharman, Raj; Mulgund, Pavankumar; Parthiban, Lakshmi Prithika; Samant, Harshada

Abstract: People inflicted with diseases such as STDs experience a tremendous number of v ... Expand

Abstract: People inflicted with diseases such as STDs experience a tremendous number of varying emotions from the onset of symptoms to being cured/living with it. Leveraging the power of bidirectional encoder representation transformers our study builds a custom-named entity recognition model which identifies uniquely associated emotions at various stages of the disease. Our study uses over 1 million posts derived from online communities regarding STDs. We further provide analytical insights into varying emotions at different stages based on contextual reasonings. Finally, we utilize the resulting insights to produce prescriptive measures for future online recommendation systems on online communities, and for stakeholders hoping to build devices that address problems about stigmatized disorders such as STDs. Collapse

Topics: online community recommender system misinformation website Reddit

Methods: BERT computational algorithm named entity recognition machine learning language model

Analyzing the Quality of Twitter Data Streams

2022 | Information Systems Frontiers | Citations: 0

Authors: Arolfo, Franco; Rodriguez, Kevin Cortés; Vaisman, Alejandro

Abstract: There is a general belief that the quality of Twitter data streams is generally ... Expand

Abstract: There is a general belief that the quality of Twitter data streams is generally low and unpredictable, making, in some way, unreliable to take decisions based on such data. The work presented here addresses this problem from a Data Quality (DQ) perspective, adapting the traditional methods used in relational databases, based on quality dimensions and metrics, to capture the characteristics of Twitter data streams in particular, and of Big Data in a more general sense. Therefore, as a first contribution, this paper re-defines the classic DQ dimensions and metrics for the scenario under study. Second, the paper introduces a software tool that allows capturing Twitter data streams in real time, computing their DQ and displaying the results through a wide variety of graphics. As a third contribution of this paper, using the aforementioned machinery, a thorough analysis of the DQ of Twitter streams is performed, based on four dimensions: Readability, Completeness, Usefulness, and Trustworthiness. These dimensions are studied for several different cases, namely unfiltered data streams, data streams filtered using a collection of keywords, and classifying tweets referring to different topics, studying the DQ for each topic. Further, although it is well known that the number of geolocalized tweets is very low, the paper studies the DQ of tweets with respect to the place from where they are posted. Last but not least, the tool allows changing the weights of each quality dimension considered in the computation of the overall data quality of a tweet. This allows defining weights that fit different analysis contexts and/or different user profiles. Interestingly, this study reveals that the quality of Twitter streams is higher than what would have been expected. Collapse

Topics: Twitter data quality big data covid 19 application programming interface

Methods: experiment sentiment analysis literature study CoreNLP exploratory data analysis

Combining review-based collaborative filtering and matrix factorization: A solution to rating's sparsity problem

2022 | Decision Support Systems | Citations: 0

Authors: Duan, Rui; Jiang, Cuiqing; Jain, Hemant K.

Abstract: An important factor affecting the performance of collaborative filtering for rec ... Expand

Abstract: An important factor affecting the performance of collaborative filtering for recommendation systems is the sparsity of the rating matrix caused by insufficient rating data. Improving the recommendation model and introducing side information are two main research approaches to address the problem. We combine these two approaches and propose the Review-Based Matrix Factorization method in this paper. The method consists of two phases. The first phase is review-based collaborative filtering, where an item-topic rating matrix is constructed by the feature-level opinion mining of online review text. This rating matrix is used to derive item similarities, which can be used to infer unknown users' ratings of the items. The second phase consists of rating imputation, where we first fill some of the empty elements of the user-item rating matrix, then conduct matrix factorization to learn the latent user and item factors to generate recommendations. Experiments on two actual datasets show that our method improves the accuracy of recommendation compared with similar algorithms. Collapse

Topics: recommender system online review laptop computer collaborative filtering Java

Methods: computational algorithm experiment sentiment analysis data imputation topic model

What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”

2021 | Information Systems Frontiers | Citations: 0

Authors: Kar, Arpan Kumar

Abstract: Mobile payment services have become increasingly important in daily lives in Ind ... Expand

Abstract: Mobile payment services have become increasingly important in daily lives in India due to multiple planned and unplanned events. The objective of this study is to identify the determinants of usage satisfaction of mobile payments which could enhance service adoption. The “Digital Service Usage Satisfaction Model” has been proposed and validated by combining technology adoption and service science literature. First the data was extracted from Twitter based on hashtags and keywords. Then using sentiment mining and topic modelling the large volumes of text were analysed. Then network science was also used for identifying clusters among associated topics. Then, using content analysis methodology, a theoretical model was developed based on literature. Finally using multiple regression analysis, we validated the proposed model. The study establishes that cost, usefulness, trust, social influence, credibility, information privacy and responsiveness factors are more important to increase the usage satisfaction of mobile payments services. Also methodologically, this is an endeavour to validate a new approach which uses social media data for developing a inferential theoretical model. Collapse

Topics: mobile payment Twitter social influence digital service mobile system

Methods: topic model regression analysis method sentiment analysis qualitative content analysis theory development

AUTOMATED ASSISTANCE FOR DATA MODELERS: A HEURISTICS-BASED NATURAL LANGUAGE PROCESSING APPROACH

2021 | European Conference On Information Systems | Citations: 0

Authors: Ternes, Benjamin; Rosenthal, Kristina; Strecker, Stefan

Abstract: Identifiers of model elements convey semantics of conceptual models essential t ... Expand

Abstract: Identifiers of model elements convey semantics of conceptual models essential to interpretation by human viewers, e.g., the identifier 'Customer' labeling an entity type in an Entity-Relationship diagram. Devising meaningful and purposeful identifiers has proven a particular challenge for data modelers, and has repeatedly been observed among the most common difficulties learners of conceptual data modeling face. The Automated Assistant reported in this paper provides data modelers with modelingtime suggestions on identifying and signifying entity types, relationship types, and attributes with meaningful and expedient identifiers. Different from earlier approaches, the Automated Assistant implementation does not rely on fixed reference solutions for modeling tasks, and works with (m)any natural language descriptions of a universe of discourse. We report on the design artifact, the Automated Assistant, highlight two selected design iterations, demonstrate practical applications, and evaluate its performance in typical application cases. Collapse

Topics: electronic mail enterprise architecture

Methods: design artifact design science data modeling natural language processing entity relationship modeling

Large-scale Analysis of Free-Text Data for Mental Health Surveillance with Topic Modelling

2020 | Americas Conference on Information Systems | Citations: 0

Authors: Gu, Yang; Leroy, Gondy

Abstract: Autism spectrum disorder (ASD) affects 1 in 59 children in the US and costs the ... Expand

Abstract: Autism spectrum disorder (ASD) affects 1 in 59 children in the US and costs the US economy $66 billion annually. The Center for Disease Control and Prevention (CDC) has collected a large set of EHR as part of surveillance in the US. In Arizona, the dataset contains 4480 EHR with 10 million free text tokens over ten years. It contains detailed descriptions of children with ASD-like behaviors. While the knowledge about and the diagnostic criteria of ASD have evolved, the data collected from earlier years have not been re-evaluated. To more efficiently leverage this data and uncover causes for the increase in ASD prevalence observed in epidemiological surveillance, we use Latent Dirichlet Allocation (LDA) to analyze the content of the text data automatically. Preliminary results suggest LDA can model topics in EHR content and show variations in content that are consistent with changes in the data collection effort. Collapse

Topics: Python Java knowledge base

Methods: topic model computational algorithm latent dirichlet allocation NLTK part of speech tagging

Who Is the Next “Wolf of Wall Street”? Detection of Financial Intermediary Misconduct

2020 | Journal of the Association for Information Systems | Citations: 0

Authors: Lausen, Jens; Clapham, Benjamin; Siering, Michael; Gomber, Peter

Abstract: Financial intermediaries are essential for investors’ participation in financial ... Expand

Abstract: Financial intermediaries are essential for investors’ participation in financial markets. Because of their position within the financial system, intermediaries who commit misconduct not only harm investors but also undermine trust in the financial system, which ultimately has a significant negative impact on the economy as a whole. Building upon information manipulation theory and warranting theory and making use of self-disclosed data with different levels of external verification, we propose different classifiers to automatically detect financial intermediary misconduct. In particular, we focus on self-disclosed information by financial intermediaries on the business network LinkedIn. We match user profiles with regulator-disclosed information and use these data for classifier training and evaluation. We find that self-disclosed information provides valuable input for detecting financial intermediary misconduct. In terms of external verification, our classifiers achieve the best predictive performance when also taking regulator-confirmed information into account. These results are supported by an economic evaluation. Our findings are highly relevant for both investors and regulators seeking to identify financial intermediary misconduct and thus contribute to the societal challenge of building and ensuring trust in the financial system. Collapse

Topics: LinkedIn website data breach social network fraud detection

Methods: machine learning support vector machine sensitivity analysis classification method McNemar's test

Theories: information manipulation theory interpersonal deception theory

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

Facilitating Technology Transfer by Patent Knowledge Graph

2019 | HICSS | Citations: 0

Authors: Deng, Weiwei; Huang, Xiaoming; Zhu, Peihu

Abstract: Technologies are one of the most important driving forces of our societal develo ... Expand

Abstract: Technologies are one of the most important driving forces of our societal development and realizing the value of technologies heavily depends on the transfer of technologies. Given the importance of technologies and technology transfer, an increasingly large amount of money has been invested to encourage technological innovation and technology transfer worldwide. However, while numerous innovative technologies are invented, most of them remain latent and un-transferred. The comprehension of technical documents and the identification of appropriate technologies for given needs are challenging problems in technology transfer due to information asymmetry and information overload problems. There is a lack of common knowledge base that can reveal the technical details of technical documents and assist with the identification of suitable technologies. To bridge this gap, this research proposes to construct knowledge graph for facilitating technology transfer. A case study is conducted to show the construction of a patent knowledge graph and to illustrate its benefit to finding relevant patents, the most common and important form of technologies. Collapse

Topics: knowledge graph database system

Methods: case study experiment CoreNLP paired t-test parametric test

Theories: theory of inventive problem solving