The icing on the cake - combining relational and semantic methods to extract meaning from online message board postings

. However, studies that combine different methods are scarce [9]. This holds in particular for studies that focus on relational (using social network analysis) and interpretational information (using for example semantic maps). To our knowledge, studies that employ both methods are lacking. With this paper, we want to contribute to the literature by proposing a way to combine both approaches. We illustrate the approach with data from an online community, and discuss implications for researchers

Implementasi dan Analisis Betweenness Centrality Berbasis Konten Menggunakan Algoritma Geisberger

Jejaring sosial marak digunakan oleh Usaha Kecil Menengah untuk memasarkan produk dan jasa, guna mencari user yang berpotensi menjadi buzzer dibutuhkan pengetahuan tentang Social Network Analysis (SNA). SNA digunakan untuk menganalisis interaksi dalam suatu kelompok jaringan sosial. Contoh pengembangan SNA adalah Content Based Social Network Analysis (CBSNA) yang dapat digunakan untuk menentukan rangking user berpengaruh berdasarkan relasi kesamaan konten. Salah satu metode penghitungan centrality adalah metode Linear Scaling yang dikembangkan oleh Geisberger, dimana dalam menghitung betweenness centrality cukup menggunakan beberapa node sebagai sumber. Pada penelitian tugas akhir ini metode yang digunakan adalah Linear Scaling yang dipadukan dengan Vector Space Model, pertama bertujuan untuk menghitung betweenness centrality berbasis konten pada studi kasus media sosial Twitter dan yang kedua untuk menganalisis parameter yang berpengaruh pada metode Linear Scaling dalam penghitungan nilai betweenness centrality. Hasil pengujian menunjukkan bahwa user dengan nilai similarity tinggi memiliki isi konten Quote Retweet selain itu metode Linear Scaling dipengaruhi oleh nilai pivot (k) dan jumlah edge suatu graf. Linear Scaling dapat digunakan untuk menghitung betweenness centrality guna menentukan ranking user yang berpengaruh berdasar suatu kata kunci tertentu. Kata Kunci : Usaha Kecil Menengah, Content Based Social Network Analysis, Betweeneess Centrality, Algoritma Geisberger, Linear Scaling, Vector Space Model

Agent-based analysis and mitigation of failure for cyber-physical systems

Techniques exist for assessment, modeling, and simulation of physical and cyber infrastructures, respectively; but such isolated analysis is incapable of fully capturing the interdependencies that occur when they intertwine to create a cyber-physical system (CPS). The first contribution of this doctoral research includes qualitative representation of the operation of a CPS in a single multi-agent model. Dependable operation of a CPS is contingent upon correct interpretation of data describing the state of the system. To this end, we propose agent-based semantic interpretation services that extract useful information from raw sensor data. We utilize the summary schemas model to reconcile differences in data resolution, syntax, and semantics; and to facilitate imprecise query of databases that maintain historical information, including failure mitigation techniques. Another contribution of the research is in developing ontologies that enable automated reasoning in the classification and mitigation of failures in CPS operation. As a measure of dependability, we quantify the effectiveness of our proposed ontology-based approach in identifying correct mitigation techniques. Our methodology and models are applicable to a broad range of CPSs; however, they are described in the context of intelligent water distribution networks (WDNs), which are cyber-physical critical infrastructure systems responsible for reliable delivery of potable water. We illustrate the use of game theory in agent-based decision support for allocation of water. As a precursor to empirical validation with field data, we developed an integrated cyber-physical WDN simulator using EPANET and MATLAB, and illustrate the use of this simulator in validating our agent-based model and ontology-based approach to automated mitigation of failure --Abstract, page iii

Attributing Meaning to Online Social Network Analysis for Tailored Socio-Behavioral Support Systems

Ubiquitous online social networks provide us with a unique opportunity to deliver scalable interventions for the support of lifestyle modifications in order to change behaviors that predispose toward cancer and other diseases. At the same time these networks act as rich data sources to inform our understanding of end-user needs. Traditionally, social network analysis is based on communication frequency among members. In this work, I introduce communication content as a complementary frame for studying these networks. QuitNet, an online social network developed to provide smoking cessation support is considered for analysis. Qualitative coding, automated content analysis, and network analysis were used to construct QuitNet sub-networks based on both frequency and content attributes. This merging of qualitative, quantitative, and automated methods expands the depth and breadth of existing network analysis techniques thereby allowing us to characterize the nature of communication among network members. First, grounded-theory based qualitative analysis provides a granular view of the QuitNet messages. Using automated text analysis, the communication links between network members were divided based on the similarity of the content in the exchanged messages to the identified themes. This automated analysis allowed us to expand the otherwise prohibitively labor-intensive qualitative methods to a large data sample using minimal time and resources. The follow-up one-mode and two-mode network analysis allowed us to investigate the content-specific communication patterns of QuitNet members. Qualitative analysis of the QuitNet messages identified themes ranging from “Social support”, “Progress”, and “Traditions” to “Nicotine Replacement Therapy (NRT) entries” and “Craves”. Automated annotation of messages was achieved by using a distributional approach incorporating distributional information from an outside corpus into a model of the QuitNet corpus to generate vector representations of messages. A k- nearest neighbor approach was used to infer themes relating to each message. The recall and precision measures indicate that the performance of the automated classification system is 0.77 and 0.71 for high-level themes. The average agreement of the system with two human raters for high-level themes approached the agreement between these human coders for a subset of 100 messages suggesting that the system is a reasonable substitute for a human rater. Subsequent one-mode network analysis provided insights into different theme-based networks at population level revealing content-specific opinion leaders. Two-mode network analysis allowed us to investigate the content affiliation patterns of QuitNet users and understand the content-specific attributes of social influence on smoking abstinence. These studies provide insights into the nature of communication among members in a smoking cessation related online social network. Ability to identify critical nodes and content-specific network patterns of communication has implications for the development and maintenance of support networks for health behavior change. Analysis of the frequency and content of health-related social network data can inform the development of tailored behavioral interventions that provide persuasive and targeted support for initiating or adhering to a positive behavior change

Explanatory visualization of multidimensional projections

Het verkrijgen van inzicht in grote gegevensverzalelingen (tegenwoording bekend als ‘big data’) kan gedaan worden door ze visueel af te beelden en deze visualisaties vervolgens interactief exploreren. Toch kunnen beide het aantal datapunten of metingen, en ook het aantal dimensies die elke meting beschrijven, zeer groot zijn – zoals een table met veel rijen en kolommen. Het visualiseren van dergelijke zogenaamde hoog-dimensionale datasets is zeer uitdagend. Een manier om dit te doen is door het maken van een laag (twee of drie) dimensionale afbeelding, waarin men dan zoekt naar interessante datapatronen in plaats van deze te zoeken in de oorspronkelijke hoog-dimensionale data. Technieken die dit scenario ondersteunen, de zogenaamde projecties, hebben verschillende voordelen – ze zijn visueel schaalbaar, ze werken robuust met ruizige data, en ze zijn snel. Toch is het gebruik van projecties ernstig beperkt door het feit dat ze moeilijk te interpreteren zijn. We benaderen dit problem door verschillende technieken te ontwikkelen die de interpretative vergemakkelijken, zoals het weergeven van projectiefouten en het uitleggen van projecties door middel van de oorpronkelijke hoge dimensies. Onze technieken zijn makkelijk te leren, snel te rekenen, en makkelijk toe te voegen aan elke dataexploratiescenario dat gebruik maakt van elke projectie. We demonstreren onze oplossingen met verschillende toepassingen en data van metingen, wetenschappelijke simulaties, software-engineering, en netwerken

A new content-based model for social network analysis

This paper presents a novel model for social network analysis in which, rather than analyzing the quantity of relationships (co-authorships, business relations, friendship, etc.), we analyze their communicative content. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular themes. The social analyst is then able to perform a study of the network evolution in terms of the relevant themes of collaboration, the detection of new concepts gaining popularity, and the existence of popular themes that could benefit from better cooperation. The methodology is experimented in the domain of a Network of Excellence on enterprise interoperability, INTEROP. © 2008 IEEE

A New Content-Based Model for Social Network Analysis

This paper presents a novel model for social network analysis in which, rather than analyzing the quantity of relationships (co-authorships, business relations, friendship, etc.), we analyze their communicative content. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular themes. The social analyst is then able to perform a study of the network evolution in terms of the relevant themes of collaboration, the detection of new concepts gaining popularity, and the existence of popular themes that could benefit from better cooperation. The methodology is experimented in the domain of a Network of Excellence on enterprise interoperability, INTEROP