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Articles 1 - 14 of 14
Full-Text Articles in Computer Sciences
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 …
From Traditional Markets To E-Commerce And Finally To Social Media Commerce, Ardian Hyseni
From Traditional Markets To E-Commerce And Finally To Social Media Commerce, Ardian Hyseni
UBT International Conference
These days, getting new customers is much easier than in the past. People and customers are online sharing and exchanging ideas on products and it has become easier to find products over the internet and lately; with social media, where people can look for information from reviews and comments on sites. This way has changed shopping to a social experience and is the key element to the growth of social commerce. Businesses want to connect with people and customers which they do business, also they want customers opinions and reviews. By using social media, companies can now easily create an …
Information Security Newsletter
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 …
Classification And Visualization Of Crime-Related Tweets, Ransen Niu, Jiawei Zhang, David S. Ebert
Classification And Visualization Of Crime-Related Tweets, Ransen Niu, Jiawei Zhang, David S. Ebert
The Summer Undergraduate Research Fellowship (SURF) Symposium
Millions of Twitter posts per day can provide an insight to law enforcement officials for improved situational awareness. In this paper, we propose a natural-language-processing (NLP) pipeline towards classification and visualization of crime-related tweets. The work is divided into two parts. First, we collect crime-related tweets by classification. Unlike written text, social media like Twitter includes substantial non-standard tokens or semantics. So we focus on exploring the underlying semantic features of crime-related tweets, including parts-of-speech properties and intention verbs. Then we use these features to train a classification model via Support Vector Machine. The second part is to utilize visual …
Informational Power On Twitter: A Mixed-Methods Exploration Of User Knowledge And Technological Discourse About Information Flows, Nicholas John Proferes
Informational Power On Twitter: A Mixed-Methods Exploration Of User Knowledge And Technological Discourse About Information Flows, Nicholas John Proferes
Theses and Dissertations
Following a number of recent examples where social media users have been confronted by information flows that did not match their understandings of the platforms, there is a pressing need to examine public knowledge of information flows on these systems, to map how this knowledge lines up against the extant flows of these systems, and to explore the factors that contribute to the construction of knowledge about these systems. There is an immediacy to this issue because as social media sites become further entrenched as dominant vehicles for communication, knowledge about these technologies will play an ever increasing role in …
Characterizing Silent Users In Social Media Communities, Gong Wei, Ee-Peng Lim, Feida Zhu
Characterizing Silent Users In Social Media Communities, Gong Wei, 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 …
Semtiment Analysis On Youtube: A Brief Survey, Dr. Muhammad Zubair Asghar, Fazal Masud Kundi, Afsana Khan
Semtiment Analysis On Youtube: A Brief Survey, Dr. Muhammad Zubair Asghar, Fazal Masud Kundi, Afsana Khan
Dr. Muhammad Zubair Asghar
Sentiment analysis or opinion mining is the field of study related to analyze opinions, sentiments, evaluations, attitudes, and emotions of users which they express on social media and other online resources. The revolution of social media sites has also attracted the users towards video sharing sites, such as YouTube. The online users express their opinions or sentiments on the videos that they watch on such sites. This paper presents a brief survey of techniques to analyze opinions posted by users about a particular video.
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 …
Features For Ranking Tweets Based On Credibility And Newsworthiness, Jacob W. Ross
Features For Ranking Tweets Based On Credibility And Newsworthiness, Jacob W. Ross
Browse all Theses and Dissertations
We create a robust and general feature set for learning to rank algorithms that rank tweets based on credibility and newsworthiness. In previous works, it has been demonstrated that when the training and testing data are from two distinct time periods, the ranker performs poorly. We improve upon previous work by creating a feature set that does not over fit a particular year or set of topics. This is critical given how people utilize social media changes as time progresses, and the topics discussed vary. In addition, we are constantly gaining new tweet data. Thus, it is important to be …
Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit
Mining Behavior Of Citizen Sensor Communities To Improve Cooperation With Organizational Actors, Hemant Purohit
Browse all Theses and Dissertations
Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including 'articulation' …