01531nas a2200133 4500008004100000245010800041210006900149260000900218520098900227653002301216100001501239700001901254856012401273 2022 eng d 00 aCombating False Information by Sharing the Truth: A Study on the Spread of Fact-checks on Social Media 0 aCombating False Information by Sharing the Truth A Study on the c2022 3 aMisinformation on social media has become a horrendous problem in our society. Fact-checks on information often fall behind the diffusion of misinformation, which can lead to negative impacts on society. This research studies how different factors may affect the spread of fact-checks over the internet. We collected a dataset of fact-checks in a six-month period and analyzed how they spread on Twitter. The spread of fact-checks is measured by the total retweet count. The factors/variables include the truthfulness rating, topic of information, source credibility, etc. The research identifies truthfulness rating as a significant factor: conclusive fact-checks (either true or false) tend to be shared more than others. In addition, the source credibility, political leaning, and the sharing count also affect the spread of fact-checks. The findings of this research provide practical insights into accelerating the spread of the truth in the battle against misinformation online. 10 aBusiness Analytics 1 aLi, Jiexun 1 aChang, Xiaohui u/biblio/combating-false-information-sharing-truth-study-spread-fact-checks-social-media 01524nas a2200133 4500008004100000245010400041210006900145260000900214520098900223653002301212100001501235700001901250856012101269 2022 eng d 00 aCombating Misinformation by Sharing the Truth: a Study on the Spread of Fact-Checks on Social Media 0 aCombating Misinformation by Sharing the Truth a Study on the Spr c2022 3 aMisinformation on social media has become a horrendous problem in our society. Fact-checks on information often fall behind the diffusion of misinformation, which can lead to negative impacts on society. This research studies how different factors may affect the spread of fact-checks over the internet. We collected a dataset of fact-checks in a six-month period and analyzed how they spread on Twitter. The spread of fact-checks is measured by the total retweet count. The factors/variables include the truthfulness rating, topic of information, source credibility, etc. The research identifies truthfulness rating as a significant factor: conclusive fact-checks (either true or false) tend to be shared more than others. In addition, the source credibility, political leaning, and the sharing count also affect the spread of fact-checks. The findings of this research provide practical insights into accelerating the spread of the truth in the battle against misinformation online. 10 aBusiness Analytics 1 aLi, Jiexun 1 aChang, Xiaohui u/biblio/combating-misinformation-sharing-truth-study-spread-fact-checks-social-media 00673nas a2200181 4500008004100000245009400041210006900135260002300204653000800227653002300235653001200258653003200270100001600302700001500318700001700333700001500350856012600365 2020 eng d 00 aImpact of Team Size on Technological Contributions: Unpacking Disruption and Development 0 aImpact of Team Size on Technological Contributions Unpacking Dis aVancouver CA c2020 10 aBIS 10 aBusiness Analytics 10 aFinance 10 aStrategy & Entrepreneurship 1 aChen, Jiyao 1 aShao, Rong 1 aFan, Shaokun 1 aLi, Jiexun u/biblio/impact-team-size-technological-contributions-unpacking-disruption-and-development 02320nas a2200157 4500008004100000245009900041210006900140260000900209300001400218490000700232520173700239653002301976100001501999700001902014856012902033 2020 eng d 00 aImproving Mobile Health Apps Usage: A Quantitative Study on mPower Data of Parkinson's Disease 0 aImproving Mobile Health Apps Usage A Quantitative Study on mPowe c2020 a399–420 0 v34 3 aPurpose
The emergence of mobile health (mHealth) products has created a capability of monitoring and managing the health of patients with chronic diseases. These mHealth technologies would not be beneficial unless they are adopted and used by their target users. This study identifies key factors affecting the usage of mHealth apps based on user usage data collected from an mHealth app.
Design/methodology/approach
Using a data set collected from an mHealth app named mPower, developed for patients with Parkinson’s disease (PD), this paper investigated the effects of disease diagnosis, disease progression, and mHealth app difficulty level on app usage, while controlling for user information. App usage is measured by five different activity counts of the app.
Findings
The results across five measures of mHealth app usage vary slightly. On average, previous professional diagnosis and high user performance scores encourage user participation and engagement, while disease progression hinders app usage.
Research limitations/implications
The findings potentially provide insights into better design and promotion of mHealth products and improve the capability of health management of patients with chronic diseases.
Originality/value
Studies on the mHealth app usage are critical but sparse because large-scale and reliable mHealth app usage data are limited. Unlike earlier works based solely on survey data, this research used a large user usage data collected from an mHealth app to study key factors affecting app usage. The methods presented in this study can serve as a pioneering work for the design and promotion of mHealth technologies. 10 aBusiness Analytics 1 aLi, Jiexun 1 aChang, Xiaohui u/biblio/improving-mobile-health-apps-usage-quantitative-study-mpower-data-parkinsons-disease 01160nas a2200157 4500008004100000245007000041210006900111260000900180300001200189490000800201520062900209653002300838100001900861700001500880856010700895 2019 eng d 00 aBusiness Performance Prediction in Location-based Social Commerce 0 aBusiness Performance Prediction in Locationbased Social Commerce c2019 a112-123 0 v126 3 aSocial commerce and location-based services provide a data platform for coexisting and competing businesses in geographical neighborhoods. Our research is aimed at mining data from such platforms to gain valuable insights for better support to strategic and operational business decisions. We develop a computational framework for predicting business performance that takes into account both intrinsic (e.g., attributes) and extrinsic (e.g., competitions) factors. Our experiments on synthetic and real datasets demonstrated superiority of a hybrid prediction model that adopts both link-based and context-based assumptions. 10 aBusiness Analytics 1 aChang, Xiaohui 1 aLi, Jiexun u/biblio/business-performance-prediction-location-based-social-commerce 00552nas a2200169 4500008004100000245006300041210006200104260000900166653000800175653002300183653001700206100001500223700001600238700001300254700001600267856009900283 2018 eng d 00 aMaking Sense of Organization Dynamics Using Text Analysis. 0 aMaking Sense of Organization Dynamics Using Text Analysis c2018 10 aBIS 10 aBusiness Analytics 10 aSupply Chain 1 aLi, Jiexun 1 aWu, Zhaohui 1 aZhu, Bin 1 aXu, Kaiquan u/biblio/making-sense-organization-dynamics-using-text-analysis 00544nas a2200157 4500008004100000245006500041210006500106260002300171653000800194653002300202653001700225100001500242700001600257700001300273856010000286 2015 eng d 00 aMining Hidden Organizational Structures from Meeting Records 0 aMining Hidden Organizational Structures from Meeting Records aPhiladelphia c2015 10 aBIS 10 aBusiness Analytics 10 aSupply Chain 1 aLi, Jiexun 1 aWu, Zhaohui 1 aZhu, Bin u/biblio/mining-hidden-organizational-structures-meeting-records 00559nas a2200145 4500008004100000245008200041210006900123260003100192653000800223653002300231100001500254700001200269700001300281856011900294 2014 eng d 00 aCollective opinion classification: A global consistency maximization approach 0 aCollective opinion classification A global consistency maximizat aAukland, New Zealand c2014 10 aBIS 10 aBusiness Analytics 1 aLi, Jiexun 1 aLi, Xin 1 aZhu, Bin u/biblio/collective-opinion-classification-global-consistency-maximization-approach