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26 results for “social worlds”
Detection of Real-World Influence through Social Media
<p><strong>Description. </strong>This dataset corresponds to the resources produced for the following conference paper and its extended version:</p> <ol> <li>J.-V. Cossu, N. Dugué, and V. Labatut, “Detecting Real-World Influence Through Twitter,” in <em>2nd European Network Intelligence Conference (ENIC)</em>, 2015, pp. 83–90. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01164453">hal-01164453</a>⟩ DOI: <a href="http://doi.org/10.1109/ENIC.2015.20">10.1109/ENIC.2015.20</a></li> <li>J.-V. Cossu, V. Labatut, and N. Dugué, “A Review of Features for the Discrimination of Twitter Users: Application to the Prediction of Offline Influence,” <em>Social Network Analysis and Mining </em>6:25, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01203171">hal-01203171</a>⟩ DOI: <a href="http://doi.org/10.1007/s13278-016-0329-x">10.1007/s13278-016-0329-x</a></li> </ol> <p>Raw data are available through the official RepLab page: <a href="http://nlp.uned.es/replab2014/">http://nlp.uned.es/replab2014/</a> (follow <a href="http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz">http://nlp.uned.es/replab2014/replab2014-dataset.tar.gz</a>)</p> <p><strong>Source code. </strong>The source code used to generate these output is available on GitHub: <a href="https://github.com/CompNet/Influence">https://github.com/CompNet/Influence</a></p> <p><strong>Funding. </strong>This work was partly funded by the French National Research Agency (ANR), through the project <a href="https://anr.fr/Project-ANR-12-CORD-0002">ImagiWeb ANR-12-CORD-0002</a>.</p> <p><strong>Contact. </strong>Jean-Valère Cossu <<a href="mailto:jean-valere.cossu@alumni.univ-avignon.fr">jean-valere.cossu@alumni.univ-avignon.fr</a>></p> <p><strong>Citation. </strong>If you use these data, please cite paper [1] above.</p> <p><br><code>@InProceedings{Cossu2015,</code><br><code> author = {Cossu, Jean-Valère and Dugué, Nicolas and Labatut, Vincent},</code><br><code> title = {Detecting Real-World Influence Through {Twitter}},</code><br><code> booktitle = {2\textsuperscript{nd} European Network Intelligence Conference},</code><br><code> year = {2015},</code><br><code> pages = {83-90},</code><br><code> address = {Karlskrona, SE},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ENIC.2015.20},</code><br><code>}</code></p> <p><strong>Details. </strong>This archive contains all ranking outputs formatted according to the TREC-EVAL tool format. These outputs consist for each domain in a ranked list of user from the most influential to the least influential. For a classification-type evaluation, just consider that users having a score higher than 0.5 are influential.</p> <p>File names correspond to the system (those starting with Cos*, indicate: the method BoT for Bag-of-Tweets, UaD for User-as-Document; the use of the Tweet-Selection strategy files denoted Artex; the learning process with Global or separated models which are noted Multi and last but not least the decision strategy for Bag-of-Tweets: Counting or Sum) or feature name. Files starting with out_* contain the results of logistic regression ranking outputs. Files matrix_auto.dat and matrix_bank.dat contain the data used to feed the PLS model (code: plspm4influence.R).</p> <p>RepLab 2014 uses Twitter data in English and Spanish. The balance between both languages depends on the availability of data for each of the profiles included in the dataset.</p> <p>The training dataset consists of 7,000 Twitter profiles (all with at least 1,000 followers) related to the automotive and banking domains, evaluation is performed separately. Each profile consists of (i) author name; (ii) profile URL and (iii) the last 600 tweets published by the author at crawling time and have been manually labelled by reputation experts either as “opinion maker” (i.e. authors with reputational influence) or “non-opinion maker”. The objective is to find out which authors have more reputational influence (who the opinion makers are) and which profiles are less influential or have no influence at all. </p> <p>Since Twitter ToS do not allow redistribution of tweets, only tweets ids and screen names are provided. Replab organizers provide details about how to download the tweets.</p>
Fig. 4 in Two new species of Xenos (Strepsiptera: Xenidae), parasites of social wasps of the genus Mischocyttarus (Hymenoptera: Vespidae) in the New World
Fig. 4. Xenos pallens Benda & Straka sp. nov., female, cephalothorax, male, cephalotheca. A – ventral side of cephalothorax; B – dorsal side of cephalothorax; C – frontal view of cephalotheca; D – lateral view of cephalotheca. Abbreviations: a – vestigial antenna, cl – clypeus, coe – compound eye, dlf – dorsal labral field of labral area, fi – frontal impression, fr – frontal region, gn – gena, hyp – hypopharynx, lba – labial area, md – mandible, mst – mesosternum, mstp – mesosternal papilla, mtst – metasternum, mtstp – metasternal papilla, mx – vestige of maxilla, mxb – maxillary base (at mandible base), mxp – vestige of maxillary palp, ob – occipital bulge, os – mouth opening, pom – postmentum, prm – prementum, pst – prosternum (prosternal extension), pstp – prosternal papilla, sbhp – segmental border between head and prothorax, sbmm – segmental border between mesothorax and metathorax, sbpm – segmental border between prothorax and mesothorax, smxg – submaxillary groove, sp – spiracle, ssf – sensillum of supraantennal sensillary field, vlf – ventral labral field of labral area.
Fig. 3 in Two new species of Xenos (Strepsiptera: Xenidae), parasites of social wasps of the genus Mischocyttarus (Hymenoptera: Vespidae) in the New World
Fig. 3. Xenos pallens Benda & Straka sp. nov., host, female, cephalothorax. A – Mischocyttarus costaricensis Richards, 1945, stylopised by X. pallens sp. nov., lateral view; B – the same specimen, dorsal view; C – holotype of X. pallens sp. nov., ventral side of cephalothorax; D – holotype of X. pallens sp. nov., dorsal side of cephalothorax. Abbreviation: cl – clypeus.
Fig. 2 in Two new species of Xenos (Strepsiptera: Xenidae), parasites of social wasps of the genus Mischocyttarus (Hymenoptera: Vespidae) in the New World
Fig. 2. Xenos bicolor Benda & Straka sp. nov., female, detail of cephalothorax, male, cephalotheca. A – detail of ventral side of cephalothorax from Mischocyttarus navajo Bequaert, 1933; B – detail of dorsal side of cephalothorax from M. flavitarsis (Saussure, 1854); C – frontal view of cephalotheca from M. pallidipectus (Smith, 1857); D – lateral view of cephalotheca from M. pallidipectus. Abbreviations: a – vestigial antenna, cl – clypeus, cll – clypeal lobe, coe – compound eye, dlf – dorsal labral field of labral area, fi – frontal impression, fr – frontal region, gn – gena, hyp – hypopharynx, lba – labial area, md – mandible, mx – vestige of maxilla, mxb – maxillary base, mxp – vestige of maxillary palp, ob – occipital bulge, os – mouth opening, pom – postmentum, prm – prementum, pst – prosternum (prosternal extension), sbhp – segmental border between head and prothorax, smxg – submaxillary groove, ssf – sensillum of supraantennal sensillary field, vlf – ventral labral field of labral area.
Fig. 1 in Two new species of Xenos (Strepsiptera: Xenidae), parasites of social wasps of the genus Mischocyttarus (Hymenoptera: Vespidae) in the New World
Fig. 1. Xenos bicolor Benda & Straka sp. nov., host, female, cephalothorax. A – Mischocyttarus flavitarsis (Saussure, 1854) stylopised by X. bicolor sp. nov., lateral view; B – detail of host abdomen of M. navajo Bequaert, 1933, with two adult females; C–D – holotype of X. bicolor sp. nov. from M. navajo, cephalothorax; C – ventral side; D – dorsal side. Abbreviations: cll – clypeal lobe, lehc – lateral extension of head capsule, mst – mesosternum, mtst – metasternum, pst – prosternum (prosternal extension), sbmm – segmental border between mesothorax and metathorax, sbpm – segmental border between prothorax and mesothorax, sp – spiracle.
Data from: Direct fitness benefits and kinship of social foraging groups in an Old World tropical babbler
Molecular studies have revealed that social groups composed mainly of non-relatives may be widespread in group-living vertebrates, but the benefits favoring such sociality are not well understood. In the Old World, birds often form conspecific foraging groups that are maintained year-round and offspring usually disperse to other social groups. We tested the hypothesis that non-breeding group members are largely unrelated and gain direct fitness benefits through breeding opportunities (males) and brood parasitism (females) in the tropical grey-throated babbler, Stachyris nigriceps, in Malaysian Borneo. Babblers foraged in social groups containing one or more breeding pairs (median = 8 group members of equal sex ratio), but group members rarely assisted with breeding (9% of 67 breeding pairs had a third helper; exhibiting facultative cooperative breeding). Although 20% of 266 group member dyads were first-order relatives of one or both members of the breeding pairs, 80% were unrelated. Male group members gained direct fitness benefits through extra-pair and extra-group paternity (25% of 73 offspring), which was independent of their relatedness to the breeding pair and increased with decreasing group size. In contrast, females did not gain direct fitness benefits through brood parasitism. The low levels of relatedness and helping in social groups suggest that most group members do not gain indirect fitness benefits by helping to raise unrelated offspring. These findings highlight the importance of examining benefits of sociality for unrelated individuals that largely do not help and broaden the direct fitness benefits of group foraging beyond assumed survival benefits.
Social interactions generate complex selection patterns in virtual worlds
<p>Understanding the influence of social interactions on individual fitness is key to improving our predictions of phenotypic evolution. However, we often overlook the different components of selection regimes arising from interactions among organisms, including social, correlational, and indirect selection. This is due to the challenging sampling efforts required in natural populations to measure phenotypes expressed during interactions and individual fitness. Furthermore, behaviours are crucial in mediating social interactions, yet few studies have explicitly quantified these selection components on behavioural traits. In this study, we capitalize on an online multiplayer videogame as a source of extensive data recording direct social interactions among prey, where prey collaborate to escape a predator in realistic ecological settings. We estimate natural and social selection and their contribution to total selection on behavioural traits mediating competition, cooperation, and predator-prey interactions. Behaviours of other prey in a group impact an individual's survival, and thus are under social selection. Depending on whether selection pressures on behaviours are synergistic or conflicting, social interactions enhance or mitigate the strength of natural selection, although natural selection remains the main driving force. Indirect selection through correlations among traits also contributed to the total selection. Thus, failing to account for the effects of social interactions and indirect selection would lead to a misestimation of the total selection acting on traits. Dissecting the contribution of each component to the total selection differential allowed us to investigate the causal mechanisms relating behaviour to fitness and quantify the importance of the behaviours of conspecifics as agents of selection. Our study emphasizes that social interactions generate complex selective regimes even in a relatively simple ecological environment.</p>
Data from: Split between two worlds: automated sensing reveals links between above- and belowground social networks in a free-living mammal
Many animals socialize in two or more major ecological contexts. In nature, these contexts often involve one situation in which space is more constrained (e.g. shared refuges, sleeping cliffs, nests, dens or burrows) and another situation in which animal movements are relatively free (e.g. in open spaces lacking architectural constraints). Although it is widely recognized that an individual's characteristics may shape its social life, the extent to which architecture constrains social decisions within and between habitats remains poorly understood. Here we developed a novel, automated-monitoring system to study the effects of personality, life-history stage and sex on the social network structure of a facultatively social mammal, the California ground squirrel (Otospermophilus beecheyi) in two distinct contexts: aboveground where space is relatively open and belowground where it is relatively constrained by burrow architecture. Aboveground networks reflected affiliative social interactions whereas belowground networks reflected burrow associations. Network structure in one context (belowground), along with preferential juvenile–adult associations, predicted structure in a second context (aboveground). Network positions of individuals were generally consistent across years (within contexts) and between ecological contexts (within years), suggesting that individual personalities and behavioural syndromes, respectively, contribute to the social network structure of these free-living mammals. Direct ties (strength) tended to be stronger in belowground networks whereas more indirect paths (betweenness centrality) flowed through individuals in aboveground networks. Belowground, females fostered significantly more indirect paths than did males. Our findings have important potential implications for disease and information transmission, offering new insights into the multiple factors contributing to social structures across ecological contexts.
Do the directions of Social Bonds intervention differentiate the World in terms of capital per beneficiary raised and scheme duration?
<p>Appendix 1 to the article <strong>Do the directions of Social Bonds intervention differentiate the World in terms of capital per beneficiary raised and scheme duration? </strong></p>
Data from: Split between two worlds: automated sensing reveals links between above- and belowground social networks in a free-living mammal
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Social interactions generate complex selection patterns in virtual worlds
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Data from: Direct fitness benefits and kinship of social foraging groups in an Old World tropical babbler
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Data on life expectancy, access to publicly funded healthcare and 10 social determinants for 196 countries and 4 major territories across the world.
<p>Supplementary data for journal article "Is life expectancy higher in countries and territories with publicly funded healthcare?: Global analysis of healthcare access and the social determinants of health" at Journal of Global Health. Data on life expectancy, access to publicly funded healthcare and 10 social determinants for 196 countries and 4 major territories across the world.</p>
Github Repository for "Social interactions generate complex selection patterns in virtual worlds"
<p>Repository for <br><strong>"Social interactions generate complex selection patterns in virtual worlds"</strong><br><em>Francesca Santostefano, Maxime Fraser Franco, Pierre Olivier Montiglio</em><br>Journal of Evolutionary Biology 2024</p>
To Test the Effectiveness and Implementation Approach of a 3-month PILI Pasifika Program Lifestyle Program With Components of Social Determinants of Health Activities in Real-world Settings (Clinical
ClinicalTrials.gov study NCT07387393. IPD Sharing: Not stated. Countries: 1. Publications: 0.
SOCIO WORLD SOCIAL RESEARCH & BEHAVIORAL SCIENCES
<p>SOCIO WORLD SOCIAL RESEARCH & BEHAVIORAL SCIENCES</p>
SOCIO WORLD SOCIAL RESEARCH & BEHAVIORAL SCIENCES
<p>SOCIO WORLD SOCIAL RESEARCH & BEHAVIORAL SCIENCES</p>
SOCIO WORLD SOCIAL RESEARCH & BEHAVIORAL SCIENCES
<h1>SOCIO WORLD SOCIAL RESEARCH & BEHAVIORAL SCIENCES</h1>
SOCIO WORLD SOCIAL RESEARCH & BEHAVIORAL SCIENCES
<h1>SOCIO WORLD SOCIAL RESEARCH & BEHAVIORAL SCIENCES</h1>
SOCIAL CAPITAL OF THE SOCIETY OF MAN IN THE ANCIENT WORLD
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