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.

An epistemic event -based correlation scheme for pervasive network management

Computer networks produce tremendous amounts of event-based data that can be collected and managed to support an increasing number of new classes of pervasive applications. Examples of such applications are network monitoring and crisis management. Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management. Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.

An Epistemic Event-based Correlation Scheme for Pervasive Network Management

Computer networks produce tremendous amounts of event-based data that can be collected and managed to support an increasing number of new classes of pervasive applications. Examples of such applications are network monitoring and crisis management. Although the problem of distributed event-based management has been addressed in the non-pervasive settings such as the Internet, the domain of pervasive networks has its own characteristics that make these results non-applicable. Many of these applications are based on time-series data that possess the form of time-ordered series of events. Such applications also embody the need to handle large volumes of unexpected events, often modified on-the-fly, containing conflicting information, and dealing with rapidly changing contexts while producing results with low-latency. Correlating events across contextual dimensions holds the key to expanding the capabilities and improving the performance of these applications. This dissertation addresses this critical challenge. It establishes an effective scheme for complex-event semantic correlation. The scheme examines epistemic uncertainty in computer networks by fusing event synchronization concepts with belief theory. Because of the distributed nature of the event detection, time-delays are considered. Events are no longer instantaneous, but duration is associated with them. Existing algorithms for synchronizing time are split into two classes, one of which is asserted to provide a faster means for converging time and hence better suited for pervasive network management. Besides the temporal dimension, the scheme considers imprecision and uncertainty when an event is detected. A belief value is therefore associated with the semantics and the detection of composite events. This belief value is generated by a consensus among participating entities in a computer network. The scheme taps into in-network processing capabilities of pervasive computer networks and can withstand missing or conflicting information gathered from multiple participating entities. Thus, this dissertation advances knowledge in the field of network management by facilitating the full utilization of characteristics offered by pervasive, distributed and wireless technologies in contemporary and future computer networks.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

A multimedia indexing and retrieval framework for multimedia database systems

The main challenges of multimedia data retrieval lie in the effective mapping between low-level features and high-level concepts, and in the individual users' subjective perceptions of multimedia content. ^ The objectives of this dissertation are to develop an integrated multimedia indexing and retrieval framework with the aim to bridge the gap between semantic concepts and low-level features. To achieve this goal, a set of core techniques have been developed, including image segmentation, content-based image retrieval, object tracking, video indexing, and video event detection. These core techniques are integrated in a systematic way to enable the semantic search for images/videos, and can be tailored to solve the problems in other multimedia related domains. In image retrieval, two new methods of bridging the semantic gap are proposed: (1) for general content-based image retrieval, a stochastic mechanism is utilized to enable the long-term learning of high-level concepts from a set of training data, such as user access frequencies and access patterns of images. (2) In addition to whole-image retrieval, a novel multiple instance learning framework is proposed for object-based image retrieval, by which a user is allowed to more effectively search for images that contain multiple objects of interest. An enhanced image segmentation algorithm is developed to extract the object information from images. This segmentation algorithm is further used in video indexing and retrieval, by which a robust video shot/scene segmentation method is developed based on low-level visual feature comparison, object tracking, and audio analysis. Based on shot boundaries, a novel data mining framework is further proposed to detect events in soccer videos, while fully utilizing the multi-modality features and object information obtained through video shot/scene detection. ^ Another contribution of this dissertation is the potential of the above techniques to be tailored and applied to other multimedia applications. This is demonstrated by their utilization in traffic video surveillance applications. The enhanced image segmentation algorithm, coupled with an adaptive background learning algorithm, improves the performance of vehicle identification. A sophisticated object tracking algorithm is proposed to track individual vehicles, while the spatial and temporal relationships of vehicle objects are modeled by an abstract semantic model. ^

The effect of cognitive load on deception

The current study applied classic cognitive capacity models to examine the effect of cognitive load on deception. The study also examined whether the manipulation of cognitive load would result in the magnification of differences between liars and truth-tellers. In the first study, 87 participants engaged in videotaped interviews while being either deceptive or truthful about a target event. Some participants engaged in a concurrent secondary task while being interviewed. Performance on the secondary task was measured. As expected, truth tellers performed better on secondary task items than liars as evidenced by higher accuracy rates. These results confirm the long held assumption that being deceptive is more cognitively demanding than being truthful. In the second part of the study, the videotaped interviews of both liars and truth-tellers were shown to 69 observers. After watching the interviews, observers were asked to make a veracity judgment for each participant. Observers made more accurate veracity judgments when viewing participants who engaged in a concurrent secondary task than when viewing those who did not. Observers also indicated that participants who engaged in a concurrent secondary task appeared to think harder than participants who did not. This study provides evidence that engaging in deception is more cognitively demanding than telling the truth. As hypothesized, having participants engage in a concurrent secondary task led to the magnification of differences between liars and truth tellers. This magnification of differences led to more accurate veracity rates in a second group of observers. The implications for deception detection are discussed.

The role of contingency and gaze direction in the emergence of social referencing

The current study assessed the importance of infant detection of contingency and head and eye gaze direction in the emergence of social referencing. Five- to six-month-old infants' detection of affect-object relations and subsequent manual preferences for objects paired with positive expressions were assessed. In particular, the role of contingency between toys' movements and an actress's emotional expressions as well as the role of gaze direction toward the toys' location were examined. Infants were habituated to alternating films of two toys each paired with an actress's affective expression (happy and fearful) under contingent or noncontingent and gaze congruent or gaze incongruent conditions. Results indicated that gaze congruence and contingency between toys' movements and a person's affective expressions were important for infant perception of affect-object relations. Furthermore, infant perception of the relation between affective expressions and toys translated to their manual preferences for the 3-dimensional toys. Infants who received contingent affective responses to the movements of the toys spent more time touching the toy that was previously paired with the positive expression. These findings demonstrate the role of contingency and gaze direction in the emergence of social referencing in the first half year of life.^

Intuition and its component parts in the heuristic processing of deception detection: Hunch, *evidence, and reliance on intuitive efficacy

Rated trust in intuitive efficacy (measured as trust, belief, use, accuracy and weighting of intuition) was investigated as a predictor of self-designated use of intuitive (hunch and hunch plus evidential belief) vs. deliberative (evidential belief and evidential belief plus hunch) deception detection judgments and actual accuracy. Twenty-nine student participants were filmed as they made true and deceptive statements about their everyday activities on a given evening (last Friday night), and college students (N=238) judged 20 (10=true, 10=deceptive) of these filmed statements as truthful or deceptive. Participants provided ratings of reliance on hunches vs. evidential belief, confidence in film judgments, intuitive efficacy, accuracy in deception detection, reliance on cues to deception, and experiences with intuition. Generalized estimated equation modeling using binary logistics demonstrated accuracy in identifying true vs. deceptive statements was predicted by film number, hunch-evidence ratings, weighting of intuition, and total cues cited. Weighting of intuition was predictive of accuracy across participants, with higher weighting predictive of higher accuracy in general. Participants who cited evidential belief plus hunch and moderate to high weighting incorrectly reversed their true vs. deceptive judgments. Accuracy for true statements was higher for hunches and hunch plus evidential belief, whereas accuracy for deceptive statements was higher for evidential belief Accuracy for participants who relied on evidential belief plus hunch was at chance. Subjective experiences underlying judgments differed by participant and type of film viewed (true vs. deceptive) and were predicted by hunch-evidence ratings, trust, use, intuitive accuracy, and total cues cited. Trust predicted increases in judging films to be true, whereas use and accuracy predicted increases in judging films as deceptive; none were predictive of accuracy. Increased number of cues cited predicted judgments of deception, whereas decreased number of cues cited predicted truth. The study concluded that participants have the capacity to self-define their judgments as subjectively vs. deliberately based, provide subjective assessments of the influence of intuitive vs. objective information on their judgments, and can apply this self-knowledge, through effective weighting of intuition vs. other types of information, in making accurate judgments of true and deceptive everyday statements.

¡Oh Cuba Hermosa! El cancionero político social en Cuba hasta 1958, Book presentation by Cristóbal Díaz Ayala

This flyer promotes the event " ¡Oh Cuba Hermosa! El cancionero político social en Cuba hasta 1958, Book presentation by Cristóbal Díaz Ayala" cosponsored by FIU Libraries. This event was held at Books & Books in Coral Gables.

A Dispersed People: Social and Cultural Dimensions of the Cuban Diaspora, Book Presentation with Volume Editor Jorge Duany

This flyer promotes the event "A Dispersed People: Social and Cultural Dimensions of the Cuban Diaspora, Book Presentation with Volume Editor Jorge Duany, comments by Lillian Manzor" sponsored by the School of International and Public Affairs at Florida International University. This event was held at Books and Books in Coral Gables.