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.

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.

No fio da meada: um estudo acerca da precarização do trabalho na indústria têxtil

In line with the process of financialization and globalization of capital, which has intensified in all latitudes of the globe, the world of work is permeated by his determinations arising and also has been (re) setting from numerous changes expressed by example, in the unbridled expansion of temporary forms of work activities, and flexible outsourced by the growth of informality, forming a new morphology of work. However, regardless of how these forms are expressed in concrete materiality, there is something that unifies: all of them are marked by exponentiation of insecurity and hence the numerous negative effects on the lives of individuals who need to sell their labor power to survive. Given this premise, the present work is devoted to study, within the framework of the Brazilian particularities of transition between Fordism and Toyotism, what we call composite settings of the conditions and labor relations processed within the North river- textile industry Grande. To this end, guided by historical and dialectical materialism, we made use of social research in its qualitative aspect, using semi-structured interviews, in addition to literature review, information retrieval and use of field notes. From our raids, we note that between the time span stretching from the 1990s to the current year, the Natal textile industry has been undergoing a process of successive and intense changes in their modus operandi, geared specifically to the organization and labor management causing, concomitantly, several repercussions for the entire working class.

No fio da meada: um estudo acerca da precarização do trabalho na indústria têxtil

In line with the process of financialization and globalization of capital, which has intensified in all latitudes of the globe, the world of work is permeated by his determinations arising and also has been (re) setting from numerous changes expressed by example, in the unbridled expansion of temporary forms of work activities, and flexible outsourced by the growth of informality, forming a new morphology of work. However, regardless of how these forms are expressed in concrete materiality, there is something that unifies: all of them are marked by exponentiation of insecurity and hence the numerous negative effects on the lives of individuals who need to sell their labor power to survive. Given this premise, the present work is devoted to study, within the framework of the Brazilian particularities of transition between Fordism and Toyotism, what we call composite settings of the conditions and labor relations processed within the North river- textile industry Grande. To this end, guided by historical and dialectical materialism, we made use of social research in its qualitative aspect, using semi-structured interviews, in addition to literature review, information retrieval and use of field notes. From our raids, we note that between the time span stretching from the 1990s to the current year, the Natal textile industry has been undergoing a process of successive and intense changes in their modus operandi, geared specifically to the organization and labor management causing, concomitantly, several repercussions for the entire working class.

Avaliação de qualidade da unidade de diálise no Hospital Universitário Onofre Lopes - HUOL

Over the years there has been a broader definition of the term health. At the same time it was found also an evolution of the concept of health care which in turn has led to changes in the approach to delivery of health services and hence in its management. In this regard, currently the nephrology services have been searching for quality technical and social need. In view of these innovations and the quest for quality, it elaborated the general objective: to develop a quality assessment protocol for dialysis service Onofre Lopes University Hospital. It is an intervention project effected through an action research, which consisted of 4 steps. Initially was identified through a literature search in scientific literature, which quality indicators would apply to a dialysis unit being selected as follows: infection rate in hemodialysis access site, microbiological control of water used for hemodialysis and Index User satisfaction. Through critical reflection on the theme researched in the previous step, it was drawn up three data collection instruments, interview form type, applied between the months of October and November 2015. In addition to the information obtained, also made up of the use of information retrieval technique. The results were organized in graphs and tables and analyzed using qualitative and exploratory technical approach. Then a reflective analysis of the data obtained and the diagnosis of reality studied was traced and confronted with the literature was performed. The data produced in this study revealed that the Dialysis Unit of HUOL is much to be desired, considering that some weaknesses have been identified in its structure. Faced with this finding have been proposed, as a contribution and aiming to guide the development of future actions, suggestions for improvement that should be implemented and monitored to be assured overcoming these difficulties, allowing an appropriate organizational restructuring, and resulting in improved service public offered. It was concluded that for hemodialysis treatment results are achieved and positive, it is necessary to have physical structure and adequate infrastructure, multidisciplinary team specialized, trained and in sufficient quantity, well designed processes for professionals to have standards to be followed decreasing the chance to err, and a risk management system to detect and control situations that endanger patient safety.

Avaliação de qualidade da unidade de diálise no Hospital Universitário Onofre Lopes - HUOL

Over the years there has been a broader definition of the term health. At the same time it was found also an evolution of the concept of health care which in turn has led to changes in the approach to delivery of health services and hence in its management. In this regard, currently the nephrology services have been searching for quality technical and social need. In view of these innovations and the quest for quality, it elaborated the general objective: to develop a quality assessment protocol for dialysis service Onofre Lopes University Hospital. It is an intervention project effected through an action research, which consisted of 4 steps. Initially was identified through a literature search in scientific literature, which quality indicators would apply to a dialysis unit being selected as follows: infection rate in hemodialysis access site, microbiological control of water used for hemodialysis and Index User satisfaction. Through critical reflection on the theme researched in the previous step, it was drawn up three data collection instruments, interview form type, applied between the months of October and November 2015. In addition to the information obtained, also made up of the use of information retrieval technique. The results were organized in graphs and tables and analyzed using qualitative and exploratory technical approach. Then a reflective analysis of the data obtained and the diagnosis of reality studied was traced and confronted with the literature was performed. The data produced in this study revealed that the Dialysis Unit of HUOL is much to be desired, considering that some weaknesses have been identified in its structure. Faced with this finding have been proposed, as a contribution and aiming to guide the development of future actions, suggestions for improvement that should be implemented and monitored to be assured overcoming these difficulties, allowing an appropriate organizational restructuring, and resulting in improved service public offered. It was concluded that for hemodialysis treatment results are achieved and positive, it is necessary to have physical structure and adequate infrastructure, multidisciplinary team specialized, trained and in sufficient quantity, well designed processes for professionals to have standards to be followed decreasing the chance to err, and a risk management system to detect and control situations that endanger patient safety.

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.

D6.3 – Semantic Content Annotation Support

The Semantic Annotation component is a software application that provides support for automated text classification, a process grounded in a cohesion-centered representation of discourse that facilitates topic extraction. The component enables the semantic meta-annotation of text resources, including automated classification, thus facilitating information retrieval within the RAGE ecosystem. It is available in the ReaderBench framework (http://readerbench.com/) which integrates advanced Natural Language Processing (NLP) techniques. The component makes use of Cohesion Network Analysis (CNA) in order to ensure an in-depth representation of discourse, useful for mining keywords and performing automated text categorization. Our component automatically classifies documents into the categories provided by the ACM Computing Classification System (http://dl.acm.org/ccs_flat.cfm), but also into the categories from a high level serious games categorization provisionally developed by RAGE. English and French languages are already covered by the provided web service, whereas the entire framework can be extended in order to support additional languages.

Clustering-based Query Routing in Cooperative Semi-structured Peer to Peer Networks

We consider the problem of resource selection in clustered Peer-to-Peer Information Retrieval (P2P IR) networks with cooperative peers. The clustered P2P IR framework presents a significant departure from general P2P IR architectures by employing clustering to ensure content coherence between resources at the resource selection layer, without disturbing document allocation. We propose that such a property could be leveraged in resource selection by adapting well-studied and popular inverted lists for centralized document retrieval. Accordingly, we propose the Inverted PeerCluster Index (IPI), an approach that adapts the inverted lists, in a straightforward manner, for resource selection in clustered P2P IR. IPI also encompasses a strikingly simple peer-specific scoring mechanism that exploits the said index for resource selection. Through an extensive empirical analysis on P2P IR testbeds, we establish that IPI competes well with the sophisticated state-of-the-art methods in virtually every parameter of interest for the resource selection task, in the context of clustered P2P IR.

Hemeroteca digital temática: socialização da informação em cinema

MEDEIROS, Rildeci; MELO, Erica S. F.; NASCIMENTO, M. S. Hemeroteca digital temática: socialização da informação em cinema.In:SEMINÁRIO NACIONAL DE BIBLIOTECAS UNIVERSITÁRIAS,15.,2008,São Paulo. Anais eletrônicos... São Paulo:CRUESP,2008. Disponível em: http://www.sbu.unicamp.br/snbu2008/anais/site/pdfs/3018.pdf

“Medical bag for General Practice” Just in Case: redesigning the doctor’s bag (2012)

This research addressed practice related problems from a medico-legal perspective and aims to provide a working tool that aids GPs to comply with best practice protocols. The resulting bag was developed in collaboration with General Practitioners, clinicians and members of the Medical Defense Union. Using proven methods developed within the Healthcare & Patient Safety Lab (e.g. DOME, Ambulance) to establish an evidence-based brief, this research used task, equipment and consumables analysis to determine minimum requirements and preferred layouts for task optimisation. The research established that clinicians require three distinct functions in their workspace: laying out, organisation and information retrieval. Feedback from clinicians indicates that this working tool allows them to access information and equipment wherever they may be and suggests an improvement from current practice. The research is now into a second year where the design of the bag will be refined and tested. Lifestyle and demographic changes such as the ageing population and increased prevalence of chronic diseases require more consistent standards of primary care, and care that is well coordinated and integrated (Imison, et al., 2011). Many guidelines exist relating to general practice and the doctor’s bag (NSLMC, 2008, RACGP, 2010, RCGP, 2008 and Hiramanek, 2004), however there is no standard in the UK that regulates the shape and materials of the bag or its contents. Doctors may use any sort of vessel to transport their equipment and consumables to a patient’s location. Furthermore, treating a patient in their own home, outside an ideal clinical environment, presents its own complications. A looks-like, works-like bag prototype and information system that will be used in clinical trials, the results of which will determine the manufacturing of a new, standardised bag for clinical treatment used by members of the Medical Defence Union.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Hemeroteca digital temática: socialização da informação em cinema

MEDEIROS, Rildeci; MELO, Erica S. F.; NASCIMENTO, M. S. Hemeroteca digital temática: socialização da informação em cinema.In:SEMINÁRIO NACIONAL DE BIBLIOTECAS UNIVERSITÁRIAS,15.,2008,São Paulo. Anais eletrônicos... São Paulo:CRUESP,2008. Disponível em: http://www.sbu.unicamp.br/snbu2008/anais/site/pdfs/3018.pdf

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.

Using Kullback-Leibler Distance for Text Categorization

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