How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model

2023 | Information Systems Frontiers | Citations: 0

Authors: Aghakhani, Navid; Oh, Onook; Gregg, Dawn; Jain, Hemant

Abstract: This study extends our understanding of what makes an online review useful by ex ... Expand

Abstract: This study extends our understanding of what makes an online review useful by examining the effects of review quality (i.e., as a composite variable of review comprehensiveness and review topic consistency) on review usefulness, and the moderating effects of source credibility on the relationship between review quality and review usefulness. The Elaboration Likelihood Model, convergence theory, and cueing effect literature are used to define the variables of review comprehensiveness and review topic consistency. Analyses of 27,517 restaurant reviews from Yelp show that review topic consistency has a positive effect on review usefulness, but, contrary to our hypothesis, review comprehensiveness has a negative effect on review usefulness. We also found source credibility positively moderates the effect of review comprehensiveness on review usefulness, but negatively moderates the effect of review topic consistency on review usefulness. Theoretical and practical implications are discussed. Collapse

Topics: online review user-generated content Python website data breach

Methods: topic model term frequency–inverse document frequency NLTK statistical hypothesis test robustness check

Theories: elaboration likelihood model

User Privacy, Surveillance and Public Health during COVID-19 – An Examination of Twitterverse

2023 | Information Systems Frontiers | Citations: 0

Authors: Bhatt, Paras; Vemprala, Naga; Valecha, Rohit; Hariharan, Govind; Rao, H. Raghav

Abstract: Online users frequently rely on social networking platforms to transmit public c ... Expand

Abstract: Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects—contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild. Collapse

Topics: privacy Twitter social media pandemic information privacy concern

Methods: topic model qualitative content analysis cluster analysis hierarchical clustering Gensim

A Collective Opinion Mechanism for Crowdfunding Recommendation

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Jin, Bih-Huang; Li, Yung-Ming; Kamolporn, Sanamlao

Abstract: ... Expand

Abstract: Collapse

Topics: crowdsourcing crowdfunding Twitter social network Python

Methods: sentiment analysis experiment topic model logistic regression parametric test

Multi-Channel Convolutional Neural Network for the Identification of Eyewitness Tweets of Disaster

2023 | Information Systems Frontiers | Citations: 0

Authors: Kumar, Abhinav; Singh, Jyoti Prakash; Rana, Nripendra P.; Dwivedi, Yogesh K.

Abstract: During a disaster, a large number of disaster-related social media posts are wid ... Expand

Abstract: During a disaster, a large number of disaster-related social media posts are widely disseminated. Only a small percentage of disaster-related information is posted by eyewitnesses. The post of a disaster eyewitness offers an accurate depiction of the disaster. Therefore, the information posted by the eyewitness is preferred over the other source of information as it is more effective at helping organize rescue and relief operations and potentially saving lives. In this work, we propose a multi-channel convolutional neural network (MCNN) that uses three different word-embedding vectors together to classify disaster-related tweets into eyewitness, non-eyewitness, and don’t know classes. We compared the performance of the proposed multi-channel convolutional neural network with several attention-based deep-learning models and conventional machine learning-models such as recurrent neural network, gated recurrent unit, Long-Short-Term-Memory, convolutional neural network, logistic regression, support vector machine, and gradient boosting. The proposed multi-channel convolutional neural network achieved an F1-score of 0.84, 0.88, 0.84, and 0.86 with four disaster-related datasets of floods, earthquakes, hurricanes, and wildfires, respectively. The experimental results show that the training MCNN model with different word embedding together performs better than the conventional machine-learning models and several other deep-learning models. Collapse

Topics: Twitter earthquake hurricane crowdsourcing social media

Methods: machine learning deep learning convolutional neural network experiment GloVe

Understanding customer satisfaction in curated subscription services: Moderating roles of subscription time and information disclosure

2023 | Information & Management | Citations: 0

Authors: Li, Zhuo; Zhao, Xuefeng; Ou, Grace

Abstract: A new form of online retailing, curated subscription services, is attracting an ... Expand

Abstract: A new form of online retailing, curated subscription services, is attracting an increasing number of consumers with their surprise elements. Extant literature has explored individual adoption motivation but has provided limited attention to customer-perceived consumption behavior. This study investigates the determinants of customer satisfaction with curated subscription services across different subscription box categories based on expectation disconfirmation theory. Sentiment analysis technology is conducted to extract customers’ attributelevel experiences from 22,551 customer reviews associated with 676 subscription boxes. Results show that sentiment toward utilitarian and hedonic attributes of products and services is positively associated with customer satisfaction, with the sentiment toward hedonic attributes exerting a stronger influence on satisfaction compared to utilitarian attributes. Findings also indicate that subscription time and information disclosure moderate the relationship between sentiment toward hedonic and utilitarian attributes and customer satisfac­ tion. This study enriches theoretical research on customer satisfaction, offers insights into consumer behavioral research in subscription retailing, and provides implications for managers to attract more subscribers to the curated service. Collapse

Topics: customer satisfaction data breach Python online review marketing management

Methods: sentiment analysis natural language processing support vector machine computational algorithm machine learning

Theories: expectation disconfirmation theory

Is a Pretrained Model the Answer to Situational Awareness Detection on Social Media?

2023 | HICSS | Citations: 0

Authors: Lo, S.; Lee, K.; Zhang, Y.

Abstract: Social media can be valuable for extracting information about an event or incid ... Expand

Abstract: Social media can be valuable for extracting information about an event or incident on the ground. However, the vast amount of content shared, and the linguistic variants of languages used on social media make it challenging to identify important situational awareness content to aid in decision-making for first responders. In this study, we assess whether pretrained models can be used to address the aforementioned challenges on social media. Various pretrained models, including static word embedding (such as Word2Vec and GloVe) and contextualized word embedding (such as DistilBERT) are studied in detail. According to our findings, a vanilla DistilBERT pretrained language model is insufficient to identify situation awareness information. Fine-tuning by using datasets of various event types and vocabulary extension is essential to adapt a DistilBERT model for real-world situational awareness detection. Collapse

Topics: Twitter situational awareness social media Wikipedia wiki

Methods: BERT DistilBERT support vector machine machine learning word embedding

Identifying Hidden Communities of Interest with Topic-based Networks: A Case Study of the Community of Philosophers of Science (1930-2017)

2023 | HICSS | Citations: 0

Authors: Malaterre, C.; Lareau, F.

Abstract: Scientific networks are often investigated by means of citation analyses. Yet, ... Expand

Abstract: Scientific networks are often investigated by means of citation analyses. Yet, interpretation of such networks in terms of semantic (and often disciplinary) content heavily depends on supplementary knowledge, notably about author research specialties. Similar situations arise more generally in many types of social networks whose semantic interpretation relies on supplementary information. Here, author community networks are inferred from a topic model which provides direct insights into the semantic specificities of the identified "hidden communities of interest" (HCoI). Using a philosophy of science corpus of full-text articles (N=16,917), we investigate its underlying communities by measuring topic profile correlations between authors. A diachronic perspective is built by modeling the research networks over different time-periods and mapping genealogical relationships between communities. The results show a marked increase in philosophy of science communities over time and trace the progressive appearance of the specialization areas that structure the field today. Collapse

Topics: implicit knowledge social network

Methods: topic model Gensim literature filtering computational algorithm latent dirichlet allocation

Theories: game theory

Alternative Dispute Resolution Research Landscape from 1981 to 2022

2023 | Group Decision and Negotiation | Citations: 0

Authors: Mota, Fabio Batista; Braga, Luiza Amara Maciel; Cabral, Bernardo Pereira

Abstract: Alternative dispute resolution (ADR) is an important means of resolving disputes ... Expand

Abstract: Alternative dispute resolution (ADR) is an important means of resolving disputes outside of traditional legal frameworks. It is usually adopted because of its flexibility, cost-effectiveness, and ability to preserve relationships that a contentious court battle might damage. This study aims to evaluate the scientific publication related to ADR. To do so, we used metadata of ADR-related articles from 1981 to 2022 collected in the Web of Science Core Collection and carried out a bibliometric, network, and latent Dirichlet allocation analysis. Our results indicate that ADR research is concentrated in North America, with research organizations from the United States accounting for most publications. At the same time, recent years have seen a shift to a more diverse group of countries, with the Chinese City University of Hong Kong and the Australian Victoria University leading in the last five years. The five main topics in ADR research include online dispute resolution for consumer protection, mediation for family law, arbitration in medical malpractice cases, ADR in construction projects, and ADR in employment law. The most frequent research areas assigned to ADR publications are Government & Law and Business & Economics. Network results show keywords ADR, Mediation, and Arbitration as central nodes, while the Chinese and North American organizations established the most frequent collaborations, addressing ADR applications in various sectors. The findings underscore the interdisciplinary nature of ADR research, its adaptability across industries, and the importance of cross-cultural research partnerships. Collapse

Topics: Python information system use accounting

Methods: action design research network analysis literature sample bibliometric analysis latent dirichlet allocation

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

Understanding telemedicine service users’ perceptions: A text mining analysis on social media discussion

2023 | HICSS | Citations: 0

Authors: Shang, Yanyan; Kim, J. B. (Joo Baek); Shin, Soo Il

Abstract: Telemedicine has drawn noticeable attention due to the advancement of informatio ... Expand

Abstract: Telemedicine has drawn noticeable attention due to the advancement of information technology, and it saw a surge in popularity during the COVID-19 pandemic. This study is aimed at understanding telemedicine users’ perceptions on their care services, as well as identifying the aspects of telemedicine that can be improved to enhance users’ experience and satisfaction. Specifically, we utilized a topic modeling approach with Latent Dirichlet Allocation (LDA) to analyze telemedicine-related discussion posts on Reddit to discover the topics and themes that telemedicine service users are interested in, as well as the perceptions that users have of those topics and themes. 11 topics and 6 themes were discovered by the LDA algorithm. Lastly, we provide our suggestions and insights on how telemedicine services and practitioners can implement the themes, as well as directions for future study. Collapse

Topics: electronic health Reddit website health information system social media

Methods: topic model latent dirichlet allocation natural language processing Gensim computational algorithm