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Full-Text Articles in Physical Sciences and Mathematics
Intent Classification Of Short-Text On Social Media, Hemant Purohit, Guozhu Dong, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth
Intent Classification Of Short-Text On Social Media, Hemant Purohit, Guozhu Dong, Valerie L. Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth
Kno.e.sis Publications
Social media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for resource planning. However, effective intent mining is inherently challenging due to ambiguity in interpretation, and sparsity of relevant behaviors in social data. In this paper, we address the problem of multiclass classification of intent with a use-case of social data generated during crisis …
Two Formulas For Success In Social Media: Learning And Network Effects, Liangfei Qiu, Qian Tang, Andrew B. Whinston
Two Formulas For Success In Social Media: Learning And Network Effects, Liangfei Qiu, Qian Tang, Andrew B. Whinston
Research Collection School Of Computing and Information Systems
Recent years have witnessed an unprecedented explosion in information technology that enables dynamic diffusion of user-generated content in social networks. Online videos, in particular, have changed the landscape of marketing and entertainment, competing with premium content and spurring business innovations. In the present study, we examine how learning and network effects drive the diffusion of online videos. While learning happens through informational externalities, network effects are direct payoff externalities. Using a unique data set from YouTube, we empirically identify learning and network effects separately, and find that both mechanisms have statistically and economically significant effects on video views; furthermore, the …
Characterizing Silent Users In Social Media Communities, Wei Gong, Ee-Peng Lim, Feida Zhu
Characterizing Silent Users In Social Media Communities, Wei Gong, Ee-Peng Lim, Feida Zhu
Research Collection School Of Computing and Information Systems
Silent users often constitute a significant proportion of an online user-generated content system. In the context of social media such as Twitter, users can opt to be silent all or most of the time. They are often called the invisible participants or lurkers. As lurkers contribute little to the online content, existing analysis often overlooks their presence and voices. However, we argue that understanding lurkers is important in many applications such as recommender systems, targeted advertising, and social sensing. This research therefore seeks to characterize lurkers in social media and propose methods to profile them. We examine 18 weeks of …
Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Using Support Vector Machine Ensembles For Target Audience Classification On Twitter, Siaw Ling Lo, Raymond Chiong, David Cornforth
Research Collection School Of Computing and Information Systems
The vast amount and diversity of the content shared on social media can pose a challenge for any business wanting to use it to identify potential customers. In this paper, our aim is to investigate the use of both unsupervised and supervised learning methods for target audience classification on Twitter with minimal annotation efforts. Topic domains were automatically discovered from contents shared by followers of an account owner using Twitter Latent Dirichlet Allocation (LDA). A Support Vector Machine (SVM) ensemble was then trained using contents from different account owners of the various topic domains identified by Twitter LDA. Experimental results …
On Using Synthetic Social Media Stimuli In An Emergency Preparedness Functional Exercise, Andrew Hampton, Shreyansh Bhatt, Gary Alan Smith, Jeremy S. Brunn, Hemant Purohit, Valerie L. Shalin, John M. Flach, Amit P. Sheth
On Using Synthetic Social Media Stimuli In An Emergency Preparedness Functional Exercise, Andrew Hampton, Shreyansh Bhatt, Gary Alan Smith, Jeremy S. Brunn, Hemant Purohit, Valerie L. Shalin, John M. Flach, Amit P. Sheth
Kno.e.sis Publications
This paper details the creation and use of a massive (over 32,000 messages) artificially constructed 'Twitter' microblog stream for a regional emergency preparedness functional exercise. By combining microblog conversion, manual production, and a control set, we created a web based information stream providing valid, misleading, and irrelevant information to public information officers (PIOs) representing hospitals, fire departments, the local Red Cross, and city and county government officials. PIOs searched, monitored, and (through conventional channels) verified potentially actionable information that could then be redistributed through a personalized screen name. Our case study of a key PIO reveals several capabilities that social …
Employees’ Social Networking Site Use Impact On Job Performance: Evidence From Pakistan, Murad Moqbel, Fizza Aftab
Employees’ Social Networking Site Use Impact On Job Performance: Evidence From Pakistan, Murad Moqbel, Fizza Aftab
Information Systems Faculty Publications and Presentations
This paper reinvestigates the impact of social networking site use by employees on job performance by conducting a methodological replication of Moqbel, Nevo, and Kock (2013) using samples (N=139) from Pakistan. In both studies, social networking site use has significant effects on organizational commitment and job satisfaction, and job satisfaction also has a significant impact on job performance and organizational commitment. In comparison with the U.S., we find that social networking site use in Pakistan has no significant impact on job performance through the mediating effect of job satisfaction, yet has a significant effect on organizational commitment and job satisfaction. …
Gender-Based Violence In 140 Characters Or Fewer: A #Bigdata Case Study Of Twitter, Hemant Purohit, Tanvi Banerjee, Andrew Hampton, Valerie L. Shalin, Nayanesh Bhandutia, Amit P. Sheth
Gender-Based Violence In 140 Characters Or Fewer: A #Bigdata Case Study Of Twitter, Hemant Purohit, Tanvi Banerjee, Andrew Hampton, Valerie L. Shalin, Nayanesh Bhandutia, Amit P. Sheth
Kno.e.sis Publications
Public institutions are increasingly reliant on data from social media sites to measure public attitude and provide timely public engagement. Such reliance includes the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine big (social) data consisting of nearly fourteen million tweets collected from Twitter over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. We demonstrate the utility of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events …