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33 results for “Social Network Analysis”
Social Network analysis on European countries involved in agroecology research
<p>All the 124 (68 European and 56 Transnational) agroecology research projects identified in the mapping activities carried out by the task 1.3 of the AE4EU project were used to perform a weighted social network analysis (SNA) having the participating countries as nodes and collaborations in projects as edges.</p> <p>This dataset contains data related to this SNA and consists of two sheets:</p> <ul> <li><strong>Indexes</strong> where values of some measures for each identified country in the social network analysis are reported (number of European agroecological research projects coordinated by the country; number of transnational agroecological research projects coordinated by the country; Degree Centrality; Closeness Centrality)</li> <li><strong>Edge_weights</strong> where the weights for each edge between two countries are provided according to the times two countries cooperated together for a European or a transnational project.</li> </ul>
Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques
<p>The pathways through which primates acquire skills are a central focus of cultural evolution studies. The roles of social and genetic inheritance processes in skill acquisition are often confounded by environmental factors. Hybrid macaques from Koram Island, Thailand provide an opportunity to examine the roles of inheritance and social learning to skill acquisition within a single ecological setting. These hybrids are a cross between tool-using Burmese long-tailed (<em>Macaca</em> <em>fascicularis</em> <em>aurea</em>) and non-tool-using common long-tailed macaques (<em>Macaca</em> <em>fascicularis</em> <em>fascicularis</em>). This population provides an opportunity to explore the roles of social learning and inheritance processes while being able to exclude underlying ecological factors. Here, we investigate the roles of social learning and inheritance in tool use prevalence within this population using social network analysis and simulation. Agent-based modeling (ABM) is used to generate expectations for how social/asocial learning and inheritance structure the patterning in a social network. The results of the simulation show that various transmission mechanisms can be differentiated based on associations between individuals in a social network. The results provide an investigative framework for discussing tool-use transmission pathways in the Koram social network. By combining ABM, network analysis, and behavioral data from the field we can investigate the roles social learning and inheritance play in tool acquisition in wild primates. </p>
Implementing social network analysis to understand the socio-ecology of wildlife co-occurrence and joint interactions with humans in anthropogenic environments
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Data for: Simulation and social network analysis provide insight into the acquisition of tool behavior in hybrid macaques
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Data from: Detecting and quantifying social transmission using network-based diffusion analysis
<p>1. Although social learning capabilities are taxonomically widespread, demonstrating that freely interacting animals (whether wild or captive) rely on social learning has proved remarkably challenging.</p> <p>2. Network-based diffusion analysis (NBDA) offers a means for detecting social learning using observational data on freely interacting groups. Its core assumption is that if a target behaviour is socially transmitted, then its spread should follow the connections in a social network that reflects social learning opportunities.</p> <p>3. Here, we provide a comprehensive guide for using NBDA. We first introduce its underlying mathematical framework and present the types of questions that NBDA can address. We then guide researchers through the process of: selecting an appropriate social network for their research question; determining which NBDA variant should be used; and incorporating other variables that may impact asocial and social learning. Finally, we discuss how to interpret an NBDA model's output and provide practical recommendations for model selection.</p> <p>4. Throughout, we highlight extensions to the basic NBDA framework, including incorporation of dynamic networks to capture changes in social relationships during a diffusion and using a multi-network NBDA to estimate information flow across multiple types of social relationship.</p> <p>5. Alongside this information, we provide worked examples and tutorials demonstrating how to perform analyses using the newly developed NBDA package written in the R programming language.</p>
Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
<p>Globally important chalk streams in England are in poor ecological health, in part due to inadequate water quality. Addressing this issue requires an understanding of the governance systems that surround water quality. The complexity and uncertainty inherent in hydrological systems has led to the emergence of integrated and adaptive forms of governance. In these multi-actor governance systems, the structure of the relationships between actors (the social network) has been shown to affect governance processes and outcomes.</p> <p>Using participatory social network analysis, we mapped and analysed the social networks for the River Test and River Itchen in Hampshire, UK, to identify actors and their roles, determine the network characteristics, and identify interventions to improve governance.</p> <p>Although the results suggest a well connected network of actors from the state, private sector and civil society, we find that decision making is not decentralised. Bureaucratic governance by central state actors dominates. However, trust in these central state actors and private actors in the networks is low, which undermines collaboration and co-ordination in the network.</p> <p>Devolving authority to local actors, building trust in the networks, and improving connections to important actors could help to improve governance outcomes for water quality.</p>
Interaction-Based Behavioral Analysis in Twitter Social Network
<p>Literature studies usually use data sets consisting of data collected from many different metrics and user counts collected over different time periods. The data set used in this article was formed using completely up-to-date data obtained as a result of metrics measured in terms of scope and efficiency, sufficient and effective user counts, and filtering processes. To classify users correctly and make the classification performance high—in addition to parameters used in the literature such as tweets, account age, follower rank, average retweets and average likes—other parameters such as diameter, density, reciprocity, centralization and modularity were used. These metrics are the parameters that focus on a different area to reveal many aspects in which social network users interact. The data used to create the data set was collected from Twitter. The metric data forming the data set was extracted using Twitter Rest API V1.1 supporting search/tweet endpoints by means of the SocialBlade and Netlytic platforms.</p>
Exploring Korean adolescent stress on social media: A semantic network analysis
<p><strong>Korean Adolescent's Stress Semantic Network Analysis Project</strong></p> <p>Semantic Network Analysis for Korean Adolescent's Stress</p> <p>Input data file</p> <ul> <li>data_news.csv : News data collected from Naver(<a href="https://www.naver.com">https://www.naver.com</a>)</li> <li>data_blog.csv : Blog data collected from Naver(<a href="https://www.naver.com">https://www.naver.com</a>) and Daum(<a href="https://www.daum.net">https://www.daum.net</a>)</li> </ul> <p>Output files</p> <ul> <li>Frequency Table of Each word in Documents (<em><strong>freq_news.csv</strong></em>, <em><strong>freq_blog.csv</strong></em>)</li> <li>TF-IDF(Term Frequency-Inverse Document Frequency) Table of Each word in Documents (<em><strong>tfidf_news.csv</strong></em>, <em><strong>tf_idf_blog.csv</strong></em>)</li> <li>Frequency Table of 30 keywords in Documents (<em><strong>freq_news_30.csv</strong></em>, <em><strong>freq_blog_30.csv</strong></em>)</li> <li>DTM(Document Term Matrix) of 30 keywords in Documents (<em><strong>DTM_news_30.csv</strong></em>, <em><strong>DTM_blog_30.csv</strong></em>)</li> <li>COM(Co-Occurrence Matrix) of 30 keywords in Documents (<em><strong>COM_news_30.csv</strong></em>, <em><strong>COM_blog_30.csv</strong></em>)</li> <li>Binary COM of 30 keywords in Documents (<em><strong>BinaryCOM_news_30.csv</strong></em>, <em><strong>BinaryCOM_blog_30.csv</strong></em>)</li> <li>Centrality Table of 30 keywords in Documents (<em><strong>centrality_news_30.csv</strong></em>, <em><strong>centrality_blog_30.csv</strong></em>)</li> </ul>
Data from: Assessing behavioral associations in a hybrid zone through social network analysis: complex assortative behaviors structure associations in a hybrid quail population
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Data from: Detecting and quantifying social transmission using network-based diffusion analysis
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Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
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SciStarter: exploring project connections across the citizen science landscape: a social network analysis of shared volunteers
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Data from: Social network analysis of psychological morbidity in an urban slum of Bangladesh: a cross-sectional study based on a community census
Background Social ties are believed to play important roles in mitigating depression and anxiety, as well as fostering mental health in the population. We test this association for young urban men in Bangladesh. Methods Using a locally adapted GHQ-12 instrument, we enumerate self-reported mental health outcomes for 824 post-adolescent young men between the ages of 18 and 29 in a low-income urban community in Dhaka, Bangladesh. We further measure the social network for all our subjects and estimate the association of social network of the respondents with self-reported mental health outcomes controlling for possible confounders. Results We find there are considerable variations in both the mental health outcomes and social network across respondents. The GHQ scores (mean = 9.2, SD = 4.9) suggest significant psychological morbidity among the respondents. However, our findings imply better social ties and connections can potentially mitigate negative mental health outcomes (0.05-0.65 lower standardized GHQ score). Among other factors, being married and a recent migrant are also associated with better mental health status (0.17-0.20 and 0.16-0.17 lower standardized GHQ scores respectively). Conclusion Our results underscore the importance of social connection in providing buffer against stress and anxiety through psychosocial support from one's peer in a resource constraint urban setting. Our findings also suggest incorporating social network and ties in designing mental health policies and interventions.
Data from: Social network analysis shows direct evidence for social transmission of tool use in wild chimpanzees.
Claims of culture in animals have been stimulated by studies on a wide range of taxa revealing group-specific behavior patterns that remain stable through generations, consistent with different behavioral innovations spreading within groups by social transmission in a manner similar to human culture. In chimpanzees, 39 behaviors have been identified as 'cultural', because alternative genetic and environmental explanations for the observed regional variation appear less plausible. This interpretation is supported by experimental data from captive chimpanzee groups. However, there is no experimental evidence for social learning in the wild, nor has there been direct observation of social diffusion of spontaneously occurring behavioral innovations. Here, we document the spread of two novel tool-use variants, 'moss-sponging' and 'leaf-sponge re-use', in the Sonso chimpanzee community of Budongo Forest, Uganda. We use traditional network-based diffusion analysis (NBDA) to test whether these novel behaviors spread by social learning, as well as a newly developed dynamic version of NBDA, capable of capturing temporal aspects of acquisition, i.e. how each successive personal observations impact the subsequent acquisition of behavior. Both models provide strong evidence that diffusion patterns of moss-sponging, but not leaf-sponge re-use, are significantly better explained by social than asocial learning, therefore showing that wild chimpanzees socially learned moss-sponging from each other. The most conservative estimate of social transmission accounts for 85% of observed events with an estimated 11-fold increase in learning rate for each time a novice observed an informed individual performing moss-sponging. We conclude that group-specific behavioral variants in chimpanzees can be socially learned, suggesting this prerequisite for culture originated in a common ancestor of great apes and humans, long before the advent of modern humans.
D1.4 LITERATURE REVIEW ON SOCIAL NETWORK ANALYSIS RELATED TO TRUST IN SCIENCE
<p>This document constitutes a part of the D1.4 Social Network Analysis and includes the literature review that was conducted to investigate the methodologies used for addressing the topic of trust in science in Online Social Networks (OSNs). This review contains studies that have approached the topic of trust in science from different perspectives in OSNs examining both data from OSNs and suveys related to OSNs providing useful insights about the factors that influence public trust in science. Important findings are derived from the literature review that affect public’s trust in science, such as the political ideology, educational level, and cultural factors. Also, different methods of the studies are described such as the analysis of the text of the messages, the reactions of users, and deep learning techniques. The findings of the literature review are provided to the final document of D1.4 as they address the further analysis of the Task 1.4 Social Network Analysis</p>
Evolution of digital consumer behavior: A bibliometric analysis of the impact of social networks on purchase determinations
<p><span>In the current context of a globalized and globalized and highly digitized marketplace, the study of customer behavior has become fundamental to behavior has become fundamental to understanding the complex interactions between social, cultural and commercial between social, cultural and commercial factors that influence purchasing decisions. purchasing decisions. This research aims to analyze the the impact of social media and purchase determinations on customer behavior through a bibliometric behavior through a bibliometric study. A qualitative, descriptive, non-experimental descriptive approach with a non-experimental and longitudinal design, analyzing 1,213 documents 1,213 documents extracted from Scopus, using VOSviewer and Bibliometrix for scientific mapping and bibliometric scientific mapping and bibliometric analysis. The results reveal a rapidly growing field (13.33% per Year field (13.33% per year) with an average of 16.63 citations per document and a 28.77% co-citation rate and 28.77% of international co-authorships. Emerging trends were identified the influence of social networks on purchase intent, the impact of influencers and the importance of social commerce. Semantic análisis semantic analysis highlighted the centrality of terms such as "social media", "consumer behavior" and "purchase intention", "consumer behavior" and "purchase intention". It is concluded that the field is dynamic, interdisciplinary and globally diverse, with a growing integration of technology and consumer behavior. integration of technology and consumer behavior. The implications suggest the need for more sophisticated digital marketing strategies that consider the influence of that consider the influence of social networks, the credibility of influencers and the influencer credibility and trust, and the need for more sophisticated digital marketing strategies that consider the influence of social networks, influencer credibility and trust.</span></p>
A Framework for Improving Social Inclusion using Network Analysis and IoT-based Contact Tracing, Dataset and Source Code
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MHG4SNA: Middle high german texts annotated for social network analysis
<p><strong>Description</strong></p> <p>This corpus contains multiple middle high german texts with annotations for social network analysis. It contains annotations of: named entities and entity mentions (including partial coreference resolution), direct speech, narrator's comments. See below for further description.</p> <p>The annotated texts are part of my dissertation on social network analysis of arthurian romances. The research was developed in the context of the DH center <a href="https://www.creta.uni-stuttgart.de/">CRETA</a> at the University of Stuttgart.</p> <p> </p> <p><strong>Texts</strong></p> <ul> <li>Wolfram von Eschenbach: 'Parzival', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 11–388.</li> <li>Hartmann von Aue: 'Erec', ed. by Albert Leitzmann continued by Ludwig Wolff, 7th edition by Kurt Gärtner, Tübingen 2006 (Altdeutsche Textbibliothek 39).</li> <li>Hartmann von Aue: 'Iwein', ed. by G. F. Benecke and K. Lachmann, revised by Ludwig Wolff, 7th edition, part 1: Text, Berlin 1968.</li> <li>Wolfram von Eschenbach: 'Willehalm', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 421–640.</li> <li>'Das Rolandslied des Pfaffen Konrad', ed. by Carl Wesle, 3rd edition by Peter Wapnewski, Tübingen 1985 (Altdeutsche Textbibliothek 69).</li> </ul> <p>All texts are part of the MHDBDB (<a href="http://mhdbdb.sbg.ac.at/">Mittelhochdeutsche Begriffsdatenbank</a>).</p> <p> </p> <p><strong>Annotations</strong></p> <p>The texts contain annotations of different categories, as described in the following sections.</p> <p> 1.<em> Named Entities and Entity Mentions</em></p> <p>I annotated all namend entities and entity mentions that belong to the categories PER and LOC. PER stands for 'person' and refers to real persons as well as fictional characters. LOC stands for 'location' and includes real and fictional places.</p> <p>I annotated named entities (e.g. 'Parzival' as PER or 'Nantes' as LOC) as well as entity mentions referring to an instance of PER or LOC (e.g. 'the knight' for Parzival, or 'the city' for Nantes). I did not annotate pronouns. Entity references can contain multiple words, e.g. 'the lovely queen Ginover', and they can be nested, e.g. '[the son of [the king Gahmuret]]'.</p> <p>The annotations follow the guidelines created for multiple categories and disciplines in the context of CRETA. They are published <a href="https://www.creta.uni-stuttgart.de/cute/datenmaterial/annotationsrichtlinien-1-1/index.html">here</a>. </p> <p> 2.<em> Entity Grounding</em></p> <p>All annotated entity references are mapped to the entity instance that they refer to. E.g. the refences 'Parzival', 'Herzeloyde's son', 'the young man', 'the red knight' etc. all refer to the character instance 'Parzival'. The entity grounding takes into consideration the context of the entity mentions since one and the same expression can refer to different instances (in one context 'the king' refers to Arthur, in another context to Gahmuret)</p> <p> 3.<em> Direct Speech (DS)</em></p> <p>Passages of direct speech have been annotated by detecting quotation marks. They are tagged as 'DS'. There are a few cases of embedded direct speech (passages of direct speech containing another passage of direct speech); these cases are annotated as well.</p> <p> 4. <em>Narrator's comments (EK)</em></p> <p>As additional category I annotated passages that contain statements of the narrator, narrator's comments, extensive descriptions or digressions (e.g. an excursus to a specific topic). These passages are not part of the fictional world or lead to a pause in the timeline of events. The are annotated as 'EK' ('EK': passages that aren't part of the diegesis, 'EK2': passages that lead to a pause, e.g. comments or descriptions).</p> <p> 5. <em>Segmentation</em></p> <p>The texts are subdivided in passages of 30 verses. Since some text's editions ('Parzival', 'Willehalm') contain a formal segmentation in passages of 30 verses each, the same kind of segmentation has been transfered to the other texts. This means 'segment 1' contains the first 30 verses, 'segment 2' contains verses 31-60 and so on.</p> <p>According to the editions by Lachmann, 'Parzival' and 'Willehalm' are also subdivided in chapter-like books (Parzival: book 1 to 16, Willehalm: book 1 to 9). The other texts are similarly subdivided in chapter-like sections following common content-based divisions.</p> <p> </p> <p><strong>Social Network Analysis</strong></p> <p>The data can be used to explore and analyse the social network of the texts. SNA can be performed via gephi [4] using the gefx files.</p> <p>The social network is based on co-occurrences using a) the annotated and grounded entities, and b) the text segmentation in segments of 30 verses each. A relation between two or more entities is extracted whenever they co-occur in a segment.</p> <p> </p> <p><strong>Data downloads</strong></p> <p>The annotated texts can be downloaded in multiple formats: conll, csv, and gexf.</p> <p> 1. <em>Conll </em></p> <p>The files contain seven columns:</p> <ul> <li>(1) token,</li> <li>(2) POS-tag, tagged using a <a href="https://www.ims.uni-stuttgart.de/forschung/ressourcen/werkzeuge/pos-tag-mhg/">middle high german pos tagger</a>,</li> <li>(3) number of segment,</li> <li>(4) Entity reference annotation indicating the intance that the entity reference refers to. '-' if there is no entity reference,</li> <li>(5) EK: '1' in case there is an annotation of 'EK', '0' if not,</li> <li>(6) EK2: '1' in case there is an annotation of 'EK2', '0' if not,</li> <li>(7) DS: '1' if the token is tagged as direct speech, '0' if not.</li> </ul> <p> 2. <em>Csv</em></p> <p>The csv files contain all annotations of the category PER including entity grounding.</p> <p>The files contain the following columns:</p> <ul> <li>begin and end (start and end of the entity reference expression, character offset),</li> <li>doc_id (document id),</li> <li>buch (book number),</li> <li>quote (entity reference expression),</li> <li>coref (the entity instance that the expression refers to),</li> <li>overlap (indicates if there is an overlap, relevant for embedded entities),</li> <li>ek and ek2 (narrator's comment),</li> <li>ds (direct speech),</li> <li>space (annotations of the space where the story takes action, can be ignored here),</li> <li>segnr (number of segment),</li> <li>em (embedded),</li> <li>klasse (entity class),</li> <li>xrange (technical, relevant for annotation view).</li> </ul> <p> 3. <em>Gexf</em></p> <p>These files can be used to import the data to gephi. It is based on the annotation and grounding of entities (categorie PER). A relation between entities is based on co-occurrence (whenever two or more entities co-occur in a segment, they have a relation; with more relations, the intensitiy of their relation grows). The text segmentation is described above.</p> <p>Embedded entities are excluded. Entities mentioned in direct speech (DS) or in comments (EK) can optionally be selected or deselected. These optional filters are indicated in the name of the files.</p> <p>To visualize the graph dynamically, one can use the text segmentation as timeline.</p> <p> </p> <p>release v1.0.0: data publication in the context of my dissertation. </p>
Data from: Social network analysis of psychological morbidity in an urban slum of Bangladesh: a cross-sectional study based on a community census
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Data from: Social network analysis of mating patterns in American black bears (Ursus americanus)
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