Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

The Impact of Social Media in the Workplace

Today, individuals communicate easier and faster due to accessibility of the Internet. However, when employees are distracted with social media, it can become a concern for organizations. This paper reviews literature concerning social media and its implications at workplaces, and provides recommendations to control it, using Adams’ equity theory (1963).

Using Social Media to Build Your Academic Brand

This professional development session will review recent research on the use of social media by faculty and academic staff. The bulk of the presentation will focus on social media strategies and techniques that attendees can use to develop and build their academic brand. This session will be useful to various audiences including established faculty, new faculty and graduate students.

Can Technologies and Social Media Accelerate Cuba's Democratization?

This flyer promotes a lecture by Cuban blogger and independent journalist Yoani Sanchez, founder of the blog Generacion Y and one of Time Magazine's 100 most influential people in the world. This lecture was held at the Wertheim Performing Arts Center at FIU Modesto A. Maidique Campus on April 1,2013.

Online News Media Impact on Disaster Response: October 26, 2010 Indonesian Disaster Case Study

This paper reflects a research project on the influence of online news media (from print, radio, and televised outlets) on disaster response. Coverage on the October 2010 Indonesian tsunami and earthquake was gathered from 17 sources from October 26 through November 30. This data was analyzed quantitatively with respect to coverage intensity over time and among outlets. Qualitative analyses were also conducted using keywords and value scale that assessed the degree of positivity or negativity associated with that keyword in the context of accountability. Results yielded insights into the influence of online media on actors' assumption of accountability and quality of response. It also provided information as to the optimal time window in which advocates and disaster management specialists can best present recommendations to improve policy and raise awareness. Coverage of outlets was analyzed individually, in groups, and as a whole, in order to discern behavior patterns for a better understanding of media interdependency. This project produced analytical insights but is primarily intended as a prototype for more refined and extensive research.

Mining the online social network data: Influence, summarization, and organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.

The Impact of Different Types of Media on Tourists' Behavioral Intentions

The primary purpose of this study was to examine how much different types of media affect a tourist’s decision when choosing a destination to travel. Further, this study attempted to investigate the impact of the different types of media on a tourist’s behavioral intentions. A primary field survey was designed for this study to collect data and multivariate analysis of variance (MANOVA) was performed to analyze the data and test the hypotheses. As a result, the most influential media form for choosing a destination to travel was social media, while brochure ranked the last. Subsequently, demographic factors showed noticeable propensities for the different types of media. Lastly, media types had a significant impact on three behavioral intentions for traveling as well. Study findings are expected to provide valuable information to better utilize the media as a marketing tool for the tourism industry.

Mining the Online Social Network Data: Influence, Summarization, and Organization

Online Social Network (OSN) services provided by Internet companies bring people together to chat, share the information, and enjoy the information. Meanwhile, huge amounts of data are generated by those services (they can be regarded as the social media ) every day, every hour, even every minute, and every second. Currently, researchers are interested in analyzing the OSN data, extracting interesting patterns from it, and applying those patterns to real-world applications. However, due to the large-scale property of the OSN data, it is difficult to effectively analyze it. This dissertation focuses on applying data mining and information retrieval techniques to mine two key components in the social media data — users and user-generated contents. Specifically, it aims at addressing three problems related to the social media users and contents: (1) how does one organize the users and the contents? (2) how does one summarize the textual contents so that users do not have to go over every post to capture the general idea? (3) how does one identify the influential users in the social media to benefit other applications, e.g., Marketing Campaign? The contribution of this dissertation is briefly summarized as follows. (1) It provides a comprehensive and versatile data mining framework to analyze the users and user-generated contents from the social media. (2) It designs a hierarchical co-clustering algorithm to organize the users and contents. (3) It proposes multi-document summarization methods to extract core information from the social network contents. (4) It introduces three important dimensions of social influence, and a dynamic influence model for identifying influential users.