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Articles 1 - 14 of 14
Full-Text Articles in Social Media
Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Learning-Based Stock Trending Prediction By Incorporating Technical Indicators And Social Media Sentiment, Zhaoxia Wang, Zhenda Hu, Fang Li, Seng-Beng Ho, Erik Cambria
Research Collection School Of Computing and Information Systems
Stock trending prediction is a challenging task due to its dynamic and nonlinear characteristics. With the development of social platform and artificial intelligence (AI), incorporating timely news and social media information into stock trending models becomes possible. However, most of the existing works focus on classification or regression problems when predicting stock market trending without fully considering the effects of different influence factors in different phases. To address this gap, this research solves stock trending prediction problem utilizing both technical indicators and sentiments of the social media text as influence factors in different situations. A 3-phase hybrid model is proposed …
Does Social Media Accelerate Product Recalls? Evidence From The Pharmaceutical Industry, Yang Gao, Wenjing Duan, Huaxia Rui
Does Social Media Accelerate Product Recalls? Evidence From The Pharmaceutical Industry, Yang Gao, Wenjing Duan, Huaxia Rui
Research Collection School Of Computing and Information Systems
Social media has become a vital platform for voicing product-related experiences that may not only reveal product defects but also impose pressure on firms to act more promptly than before. This study scrutinizes the rarely-studied relationship between these voices and the speed of product recalls in the context of the pharmaceutical industry where social media pharmacovigilance is becoming increasingly important for the detection of drug safety signals. Using Federal Drug Administration (FDA) drug enforcement reports and social media data crawled from online forums and Twitter, we investigate whether social media can accelerate the product recall process in the context of …
New-Media Advertising And Retail Platform Openness, Jianqing Chen, Zhiling Guo
New-Media Advertising And Retail Platform Openness, Jianqing Chen, Zhiling Guo
Research Collection School Of Computing and Information Systems
We recently have witnessed two important trends in online retailing: the advent of new media (e.g., social media and search engines) makes advertising affordable for small sellers, and large online retailers (e.g., Amazon and JD.com) opening their platforms to allow even direct competitors to sell on their platforms. We examine how new-media advertising affects retail platform openness. We develop a game-theoretic model in which a leading retailer, who has both valuation and awareness advantages, and a third-party seller, who sells an identical product, engage in price competition. We find that the availability of relatively low-cost advertising through new media plays …
Online Content Consumption: Social Endorsements, Observational Learning And Word-Of-Mouth, Qian Tang, Tingting Song, Liangfei Qiu, Ashish Agarwal
Online Content Consumption: Social Endorsements, Observational Learning And Word-Of-Mouth, Qian Tang, Tingting Song, Liangfei Qiu, Ashish Agarwal
Research Collection School Of Computing and Information Systems
The consumption of online content can occur through observational learning (OL) whereby consumers follow previous consumers’ choices or social endorsement (SE) wherein consumers receive content sharing from their social ties. As users consume content, they also generate post-consumption word-of-mouth (WOM) signals. OL, SE and WOM together shape the diffusion of the content. This study examines the drivers of SE and the effect of SE on content consumption and post-consumption WOM. In particular, we compare SE with OL. Using a random sample of 8,945 new videos posted on YouTube, we collected a multi-platform dataset consisting of data on video consumption and …
Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak
Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak
Research Collection School Of Computing and Information Systems
Gender and racial diversity in the mediated images from the media shape our perception of different demographic groups. In this work, we investigate gender and racial diversity of 85,957 advertising images shared by the 73 top international brands on Instagram and Facebook. We hope that our analyses give guidelines on how to build a fully automated watchdog for gender and racial diversity in online advertisements.
Do Your Friends Make You Buy This Brand?: Modeling Social Recommendation With Topics And Brands, Minh Duc Luu, Ee Peng Lim
Do Your Friends Make You Buy This Brand?: Modeling Social Recommendation With Topics And Brands, Minh Duc Luu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
Consumer behavior and marketing research have shown that brand has significant influence on product reviews and product purchase decisions. However, there is very little work on incorporating brand related factors into product recommender systems. Meanwhile, the similarity in brand preference between a user and other socially connected users also affects her adoption decisions. To integrate seamlessly the individual and social brand related factors into the recommendation process, we propose a novel model called Social Brand–Item–Topic (SocBIT). As the original SocBIT model does not enforce non-negativity, which poses some difficulty in result interpretation, we also propose a non-negative version, called SocBIT(Formula …
Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu
Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu
Research Collection School Of Computing and Information Systems
Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly …
Helping Smes Understand Consumers And Competitors, Kyong Jin Shim
Helping Smes Understand Consumers And Competitors, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
No abstract provided.
Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei
Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei
Research Collection School Of Computing and Information Systems
From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example, sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the "sparsity problem," but is still far …
Social Media For Supply Chain Risk Management, Xiuju Fu, Rick S. M. Goh, J. C. Tong, Loganathan Ponnanbalam, Xiaofeng Yin, Zhaoxia Wang, H. Y. Xu, Sifei Lu
Social Media For Supply Chain Risk Management, Xiuju Fu, Rick S. M. Goh, J. C. Tong, Loganathan Ponnanbalam, Xiaofeng Yin, Zhaoxia Wang, H. Y. Xu, Sifei Lu
Research Collection School Of Computing and Information Systems
With the rapid increase of online social network users worldwide, social media feeds have become a rich and valuable information resource and attract great attention across diversified domains. In social media data, there are abundant contents of two-way and interactive communication about products, demand, customer services and supply. This makes social media a valuable channel for listening to the voices from the market and measuring supply chain risks and new market trends for companies. In this study, we surveyed the potential value of social media in supply chain risk management (SCRM) and examined how they can be applied to SCRM …
Manipulation Of Online Reviews: An Analysis Of Ratings, Readability, And Sentiments, Nan Hu, Indranil Bose, Noi Sian Koh, Ling Liu
Manipulation Of Online Reviews: An Analysis Of Ratings, Readability, And Sentiments, Nan Hu, Indranil Bose, Noi Sian Koh, Ling Liu
Research Collection School Of Computing and Information Systems
As consumers become increasingly reliant on online reviews to make purchase decisions, the sales of the product becomes dependent on the word of mouth (WOM) that it generates. As a result, there can be attempts by firms to manipulate online reviews of products to increase their sales. Despite the suspicion on the existence of such manipulation, the amount of such manipulation is unknown, and deciding which reviews to believe in is largely based on the reader's discretion and intuition. Therefore, the success of the manipulation of reviews by firms in generating sales of products is unknown. In this paper, we …
Content Contribution Under Revenue Sharing And Reputation Concern In Social Media: The Case Of Youtube, Qian Tang, Bin Gu, Andrew B. Whinston
Content Contribution Under Revenue Sharing And Reputation Concern In Social Media: The Case Of Youtube, Qian Tang, Bin Gu, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
A key feature of social media is that it allows individuals and businesses to contribute contents for public viewing. However, little is known about how content providers derive payoffs from such activities. In this study, we build a dynamic structural model to recover the utility function for content providers. Our model distinguishes short-term payoffs based on ad revenue sharing from long-term payoffs driven by content providers’ reputation. The model was estimated using a panel data of 914 top 1000 providers and 381 randomly selected providers on YouTube from Jun 7th, 2010, to Aug 7th, 2011. The two different sets of …
Manipulation In Digital Word-Of-Mouth: A Reality Check For Book Reviews, Nan Hu, Indranil Bose, Yunjun Gao, Ling Liu
Manipulation In Digital Word-Of-Mouth: A Reality Check For Book Reviews, Nan Hu, Indranil Bose, Yunjun Gao, Ling Liu
Research Collection School Of Computing and Information Systems
Built upon the discretionary accrual-based earnings management framework, our paper develops a discretionary manipulation proxy to study the management of online reviews. We reveal that fraudulent review manipulation is a serious problem for 1) non-bestseller books; 2) books whose reviews are classified as not very helpful; 3) books that experience greater variability in the helpfulness of their online reviews; and 4) popular books as well as high-priced books. We also show that review management decreases with the passage of time. Just like fraudulent earnings management, manipulated online reviews reflect inauthentic information from which consumers might derive wrong valuation especially for …
Do Online Reviews Affect Product Sales? The Role Of Reviewer Characteristics And Temporal Effects, Nan Hu, Ling Liu, Jennifer Zhang
Do Online Reviews Affect Product Sales? The Role Of Reviewer Characteristics And Temporal Effects, Nan Hu, Ling Liu, Jennifer Zhang
Research Collection School Of Computing and Information Systems
Online product reviews provided by consumers who previously purchased products have become a major information source for consumers and marketers regarding product quality. This study extends previous research by conducting a more compelling test of the effect of online reviews on sales. In particular, we consider both quantitative and qualitative aspects of online reviews, such as reviewer quality, reviewer exposure, product coverage, and temporal effects. Using transaction cost economics and uncertainty reduction theories, this study adopts a portfolio approach to assess the effectiveness of the online review market. We show that consumers understand the value difference between favorable news and …