Detecting Textual Propaganda Using Machine Learning Techniques

سيطرت الشبكات الاجتماعية على العالم بأسره من خلال توفير منصة لنشر المعلومات. عادة ما يشارك الناس المعلومات دون معرفة صدقها. في الوقت الحاضر ، تُستخدم الشبكات الاجتماعية لاكتساب النفوذ في العديد من المجالات مثل الانتخابات والإعلانات وما إلى ذلك ، وليس من المستغرب أن تصبح وسائل التواصل الاجتماعي سلاحًا للتلاعب بالمشاعر من خلال نشر معلومات مُضللة. الدعاية هي إحدى المحاولات المنهجية والمتعمدة التي تستخدم للتأثير على الناس لتحقيق مكاسب سياسية ودينية. في هذه الورقة البحثية ، تم بذل جهود لتصنيف النص الدعائي من النص غير الدعائي باستخدام خوارزميات التعلم الآلي الخاضعة للإشراف. تم جمع البيانات من مصادر الأخبار في الفترة من يوليو 2018 إلى أغسطس 2018. بعد إضافة التعليقات التوضيحية على النص ، يتم تنفيذ هندسة الميزات باستخدام تقنيات مثل مصطلح تردد / تردد الوثيقة العكسي (TF / IDF) وحقيبة الكلمات (BOW). يتم توفير الميزات ذات الصلة لدعم المصنفات المتجهة (SVM) و Multinomial Naïve Bayesian (MNB). يتم إجراء ضبط دقيق لـ SVM عن طريق أخذ kernel Linear و Poly و RBF. أظهر SVM نتائج أفضل من MNB من خلال دقة 70٪ واسترجاع 76.5٪ ودرجة F1 69.5٪ ودقة كلية 69.2٪.Social Networking has dominated the whole world by providing a platform of information dissemination. Usually people share information without knowing its truthfulness. Nowadays Social Networks are used for gaining influence in many fields like in elections, advertisements etc. It is not surprising that social media has become a weapon for manipulating sentiments by spreading disinformation.  Propaganda is one of the systematic and deliberate attempts used for influencing people for the political, religious gains. In this research paper, efforts were made to classify Propagandist text from Non-Propagandist text using supervised machine learning algorithms. Data was collected from the news sources from July 2018-August 2018. After annotating the text, feature engineering is performed using techniques like term frequency/inverse document frequency (TF/IDF) and Bag of words (BOW). The relevant features are supplied to support vector machine (SVM) and Multinomial Naïve Bayesian (MNB) classifiers. The fine tuning of SVM is being done by taking kernel Linear, Poly and RBF. SVM showed better results than MNB by having precision of 70%, recall of 76.5%, F1 Score of 69.5% and overall Accuracy of 69.2%

Network communities and the foreign exchange market

Many systems studied in the biological, physical, and social sciences are composed of multiple interacting components. Often the number of components and interactions is so large that attaining an understanding of the system necessitates some form of simplication. A common representation that captures the key connection patterns is a network in which the nodes correspond to system components and the edges represent interactions. In this thesis we use network techniques and more traditional clustering methods to coarse-grain systems composed of many interacting components and to identify the most important interactions.\ud \ud This thesis focuses on two main themes: the analysis of financial systems and the study of network communities, an important mesoscopic feature of many networks. In the first part of the thesis, we discuss some of the issues associated with the analysis of financial data and investigate the potential for risk-free profit in the foreign exchange market. We then use principal component analysis (PCA) to identify common features in the correlation structure of different financial markets. In the second part of the thesis, we focus on network communities. We investigate the evolving structure of foreign exchange (FX) market correlations by representing the correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes and uncovers major trading changes that occurred in the market. Finally, we consider the community structure of networks from a wide variety of different disciplines. We introduce a framework for comparing network communities and use this technique to identify networks with similar mesoscopic structures. Based on this similarity, we create taxonomies of a large set of networks from different fields and individual families of networks from the same field

On Competition for Undergraduate Co-op Placement: A Graph Approach

The objective of this thesis is to improve the co-operative (co-op) education process by analyzing the relationships among academic programs in the context of the co-op job market. To do this, we propose and apply a novel graph-mining methodology. The input to our problem consists of student-job interview pairs, with each student labelled with his or her academic program. From this input, we build a weighted directed graph, which we refer to as a program graph, in which nodes correspond to academic programs and edge weights denote the percentage of jobs that interviewed at least one student from both programs. For example, a directed edge from the Computer Engineering program to the Electrical Engineering program with weight 0.36 means that of all the jobs that interviewed at least one Computer Engineering student, 36 percent of those jobs also interviewed at least one Electrical Engineering student. Thus, the larger the edge weight, the stronger the relationship and competition between particular programs. The output consists of various graph properties and analyses, particularly those which find nodes forming clusters or communities, nodes that are connected to few or many clusters, and nodes that are strongly connected to their immediate neighbours. As we will show, these properties have natural interpretations in terms of the relationships among academic programs and competition for co-op jobs. We applied the proposed methodology on one term of co-op interview data from a large Canadian university. We obtained interesting new insights that have not been reported in prior work. These insights can be beneficial to students, employers and academic institutions. Characterizing closely connected programs can help employers broaden their search for qualified students and can help students select programs of study that better correspond to their desired career. Students seeking a multi-disciplinary education can choose programs that are connected to other programs from many different clusters. Additionally, institutions can attend to programs that are strongly connected to (and face competition from) other programs by attracting more employers offering jobs in this area

Algoritmos bio-inspirados para la detección de comunidades dinámicas en redes complejas

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 22-07-202

Untangling Neoliberalism’s Gordian Knot: Cancer Prevention and Control Services for Rural Appalachian Populations

In eastern Kentucky, as in much of central Appalachia, current local storylines narrate the frictions and contradictions involved in the structural transition from a post-WWII Fordist industrial economy and a Keynesian welfare state to a Post-Fordist service economy and Neoliberal hollow state, starving for energy to sustain consumer indulgence (Jessop, 1993; Harvey, 2003; 2005). Neoliberalism is the ideological force redefining the “societal infrastructure of language” that legitimates this transition, in part by redefining the key terms of democracy and citizenship, as well as valorizing the market, the individual, and technocratic innovation (Chouliaraki & Fairclough, 1999; Harvey, 2005). This project develops a perspective that understands cancer prevention and control in Appalachiaas part of the structural transition that is realigning community social ties in relation to ideological forces deployed as “commonsense” storylines that “lubricate” frictions that complicates the transition

EUSN 2021 Book of Abstracts, Fifth European Conference on Social Networks

Book of abstract of the fifth European conference on Social Networks EUSN 202

Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics

Background: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18 module definitions, 129 different modularization methods, 13 module comparision methods) and 396 references. All algorithms can be downloaded from this web-site: http://www.linkgroup.hu/modules.ph

Historical social research: the use of historical and process-produced data

Die Entwicklung einer quantitativen Sozialgeschichtsschreibung verstärkt die interdisziplinären Beziehungen zwischen Geschichte, Soziologie, Politikwissenschaft und anderen Sozialwissenschaften. Diese verstärkte Kooperation und methodische Diskussion findet in dem Sammelband ihren Niederschlag. Behandelt werden (1) theoretische Überlegungen zum Problem einer quantifizierenden Geschichtswissenschaft; (2) Analysen von Volkszählungsdaten; (3) Analysen von kollektiven und individuellen Biographien; (4) Gehaltsanalysen von Dokumenten; (5) Periodisierungsprobleme; (6) Analysen des sozialen Netzwerks; (7) Probleme der offiziellen statistischen Daten; (8) Probleme der Datenorganisation; (9) neue Datenbanken und Projekte. (BG

Visual network storytelling

We love networks! Networks are powerful conceptual tools, encapsulating in a single item multiple affordances for computation (networks as graphs), visualization (networks as maps) and manipulation of data (networks as interfaces). In the field of mathematics, graph theory has been around since Euler’s walk on Königsberg’s bridges (Euler 1736). But it is not until the end of the last century that networks acquired a multidisciplinary popularity. Graph computation is certainly powerful, but it is also very demanding and for many years its advantages remained the privilege of scholars with solid mathematical fundamentals. In the last few decades, however, networks acquired a new set of affordances and reached a larger audience, thanks to the growing availability of tools to design them. Drawn on paper or screen, networks became easier to handle and obtained properties that calculation could not express. Far from being merely aesthetic, the graphical representation of networks has an intrinsic hermeneutic value. Networks can become maps and be read as such. Combining the computation power of graphs with the visual expressivity of maps and the interactivity of computer interface, networks can be used in Exploratory Data Analysis (Tukey, 1977). Navigating through data becomes so fluid that zooming in on a single data-point and out to a landscape of a million traces is just a click away. Increasingly specialized software has been designed to support the exploration of network data. Tools like Pajek (vlado.fmf.uni-lj.si/pub/networks/pajek), NetDraw (sites.google.com/site/ netdrawsoftware), Ucinet (www.analytictech.com/ucinet), Guess (graphexploration.cond.org) and more recently Gephi (gephi.org) have progressively smoothed out the difficulties of graph mathematics, turning a complex mathematical formalism into a more user-friendly point-and-click interface (1) . If visual exploration of networks can output to confirmatory statistics, what about sharing one network exploration with others? We developed Manylines (https://github.com/medialab/manylines), a tool allowing you to share the visual analysis of a network with a wide audience by publishing it on the web. With Manylines, you can not only easily publish a network on the web but also share its exploration by describing the network’s visual key findings. Through a set of examples, we will illustrate how the narrative opportunities of Manylines can contribute to the enunciation of a visual grammar of networks. (1) A simple look at the URLs of the subsequent tools reveals the efforts deployed to make network-manipulation tools user-friendly and thereby available to a larger public

The structure and dynamics of multilayer networks

In the past years, network theory has successfully characterized the interaction among the constituents of a variety of complex systems, ranging from biological to technological, and social systems. However, up until recently, attention was almost exclusively given to networks in which all components were treated on equivalent footing, while neglecting all the extra information about the temporal- or context-related properties of the interactions under study. Only in the last years, taking advantage of the enhanced resolution in real data sets, network scientists have directed their interest to the multiplex character of real-world systems, and explicitly considered the time-varying and multilayer nature of networks. We offer here a comprehensive review on both structural and dynamical organization of graphs made of diverse relationships (layers) between its constituents, and cover several relevant issues, from a full redefinition of the basic structural measures, to understanding how the multilayer nature of the network affects processes and dynamics.Comment: In Press, Accepted Manuscript, Physics Reports 201