scholarly journals Gambling in Ancient North America

2021 ◽  
Vol 2 (2) ◽  
pp. 123-140
Author(s):  
Gabriel Yanicki
Keyword(s):  
Social Networks ◽  
North America ◽  
Cultural Transmission ◽  
Knowledge Exchange ◽  
Language And Ethnicity ◽  
Over Time

Gambling in ancient North America was primarily an intergroup activity. This position as a liminal practice, taking place on territorial frontiers and at large intertribal gatherings, puts gaming on the very forefront of cultural transmission and knowledge exchange, with several implications. Intergroup gaming results in a shared fluency of games, transcending barriers of language and ethnicity. Evidence of common methods and materials allows ancient, region-spanning social networks to be identified. And subtle variations demonstrate a repeated and ongoing negotiation between groups over time as objectives and participants change, with this evolution of gaming practices continuing to the present day. The freedom to adapt to changing conditions, contrasted with notions of a static “traditional” past, is not just a matter of sovereignty relating to Indigenous games. It is a reflection of the nature of Indigenous gaming as it has always been.

scholarly journals Is there an imbalance in the supply and demand for universal accessibility knowledge? Twenty years of UAIS papers viewed through the lens of WCAG

2021 ◽  
Author(s):  
Frode Eika Sandnes
Keyword(s):  
Latin America ◽  
North America ◽  
Supply And Demand ◽  
Research Focus ◽  
Universal Accessibility ◽  
Geographical Regions ◽  
Research Profile ◽  
The Impact ◽  
Over Time

AbstractPurpose: Some universal accessibility practitioners have voiced that they experience a mismatch in the research focus and the need for knowledge within specialized problem domains. This study thus set out to identify the balance of research into the main areas of accessibility, the impact of this research, and how the research profile varies over time and across geographical regions. Method: All UAIS papers indexed in Scopus were analysed using bibliometric methods. The WCAG taxonomy of accessibility was used for the analysis, namely perceivable, operable, and understandable. Results: The results confirm the expectation that research into visual impairment has received more attention than papers addressing operable and understandable. Although papers focussing on understandable made up the smallest group, papers in this group attracted more citations. Funded research attracted fewer citations than research without funding. The breakdown of research efforts appears consistent over time and across different geographical regions. Researchers in Europe and North America have been active throughout the last two decades, while Southeast Asia, Latin America, and Middle East became active in during the last five years. There is also seemingly a growing trend of out-of-scope papers. Conclusions: Based on the findings, several recommendations are proposed to the UAIS editorial board.

scholarly journals An Exploratory Study of COVID-19 Information on Twitter in the Greater Region

2021 ◽  
Vol 5 (1) ◽  
pp. 5
Author(s):  
Ninghan Chen ◽  
Zhiqiang Zhong ◽  
Jun Pang
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Online Social Networks ◽  
Exploratory Study ◽  
Representation Learning ◽  
Data Driven ◽  
Learning Methods ◽  
Twitter Users ◽  
Over Time

The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries.

A network case of knowledge brokering

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Joelle Rodway ◽  
Stephen MacGregor ◽  
Alan Daly ◽  
Yi-Hwa Liou ◽  
Susan Yonezawa ◽  
...  
Keyword(s):  
Social Network ◽  
Learning Community ◽  
Knowledge Exchange ◽  
Point Of View ◽  
Network Activity ◽  
Centrality Measures ◽  
Knowledge Brokering ◽  
Content Type ◽  
Knowledge Brokers ◽  
Over Time

PurposeThe purpose of this paper is two-fold: (1) to offer a conceptual understanding of knowledge brokering from a sociometric point-of-view; and (2) to provide an empirical example of this conceptualization in an education context.Design/methodology/approachWe use social network theory and analysis tools to explore knowledge exchange patterns among a group of teachers, instructional coaches and administrators who are collectively seeking to build increased capacity for effective mathematics instruction. We propose the concept of network activity to measure direct and indirect knowledge brokerage through the use of degree and betweenness centrality measures. Further, we propose network utility—measured by tie multiplexity—as a second key component of effective knowledge brokering.FindingsOur findings suggest significant increases in both direct and indirect knowledge brokering activity across the network over time. Teachers, in particular, emerge as key knowledge brokers within this networked learning community. Importantly, there is also an increase in the number of resources exchanged through network relationships over time; the most active knowledge brokers in this social ecosystem are those individuals who are exchanging multiple forms of knowledge.Originality/valueThis study focuses on knowledge brokering as it presents itself in the relational patterns among educators within a social ecosystem. While it could be that formal organizational roles may encapsulate knowledge brokering across physical structures with an education system (e.g. between schools and central offices), these individuals are not necessarily the people who are most effectively brokering knowledge across actors within the broader social network.

Cracks in the Network Conversion Paradigm

2012 ◽  
Vol 3 (2) ◽  
pp. 143-162 ◽  
Author(s):  
James Lewis
Keyword(s):  
Social Networks ◽  
Change Over Time ◽  
New Religions ◽  
Dominant Point ◽  
Recruitment Patterns ◽  
Over Time

One of the standard generalizations about new religions is that people convert to NRMs primarily through preexisting social networks. The present paper examines data on a variety of new religions which demonstrates that social networks are not always the dominant point of contact for new converts. Additionally, recruitment patterns change over time so that different factors become dominant at different points in a movement’s development. Two reasons why this variability has escaped the attention of most researchers is an unconscious tendency to assume that the sociological profiles of members of different NRMs are essentially similar, and the fact that such groups are typically studied synchronically rather than diachronically.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

Quantifying Virality of Information in Online Social Networks

2012 ◽  
Vol 4 (1) ◽  
pp. 32-45 ◽  
Author(s):  
Abhishek Vaish ◽  
Rajiv Krishna G. ◽  
Akshay Saxena ◽  
Dharmaprakash M. ◽  
Utkarsh Goel
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Online Social Networks ◽  
Online Social Network ◽  
Critical Factors ◽  
Asset Valuation ◽  
Valuation Technique ◽  
Information Item ◽  
Viral Nature ◽  
Over Time

The aim of this research is to propose a model through which the viral nature of an information item in an online social network can be quantified. Further, the authors propose an alternate technique for information asset valuation by accommodating virality in it which not only complements the existing valuation system, but also improves the accuracy of the results. They use a popularly available YouTube dataset to collect attributes and measure critical factors such as share-count, appreciation, user rating, controversiality, and comment rate. These variables are used with a proposed formula to obtain viral index of each video on a given date. The authors then identify a conventional and a hybrid asset valuation technique to demonstrate how virality can fit in to provide accurate results.The research demonstrates the dependency of virality on critical social network factors. With the help of a second dataset acquired, the authors determine the pattern virality of an information item takes over time.

scholarly journals A novel similarity measure for missing link prediction in social networks

2020 ◽  
Vol 19 (2) ◽  
pp. 1071
Author(s):  
Gogulamudi Naga Chandrika ◽  
E. Srinivasa Reddy
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Similarity Measure ◽  
Network Theory ◽  
Link Prediction ◽  
Prediction Methods ◽  
Missing Link ◽  
The Social ◽  
Hidden Patterns ◽  
Over Time

<p><span>Social Networks progress over time by the addition of new nodes and links, form associations with one community to the other community. Over a few decades, the fast expansion of Social Networks has attracted many researchers to pay more attention towards complex networks, the collection of social data, understand the social behaviors of complex networks and predict future conflicts. Thus, Link prediction is imperative to do research with social networks and network theory. The objective of this research is to find the hidden patterns and uncovered missing links over complex networks. Here, we developed a new similarity measure to predict missing links over social networks. The new method is computed on common neighbors with node-to-node distance to get better accuracy of missing link prediction. </span><span>We tested the proposed measure on a variety of real-world linked datasets which are formed from various linked social networks. The proposed approach performance is compared with contemporary link prediction methods. Our measure makes very effective and intuitive in predicting disappeared links in linked social networks.</span></p>

Constructing the “Greener, Greater” City

2017 ◽  
Author(s):  
Lindsay K. Campbell
Keyword(s):  
Social Networks ◽  
Planning Process ◽  
Urban Forestry ◽  
External Factors ◽  
Central Question ◽  
Urban Nature ◽  
Sustainability Planning ◽  
Actor Networks ◽  
Internal And External Factors ◽  
Over Time

Chapter seven synthesizes these two cases, making comparisons across the three thematic areas explored in the book—politics, discourse, and materiality. It begins by returning to the central question of why urban forestry was so appealing that it merited its own signature mayoral initiative in PlaNYC, whereas urban agriculture was overlooked. It analyzes the networks, public-private partnerships, elite ties, and bureaucratic structures that were involved in PlaNYC, revealing whose voice was heard and whose voice was ignored in the sustainability planning process. Then, bearing in mind that the politics of urban nature is both framed by storylines and influenced by non-human actants, the chapter widens this analysis from a focus on politics and social networks to a focus on actor-networks and key narratives. Finally, the chapter observes how the cases shift over time in response to both internal and external factors, even within a four year window.

Constraints on Social Networks

2010 ◽  
Author(s):  
Sam G. B. Roberts
Keyword(s):  
Social Networks ◽  
Social Relationships ◽  
Human Evolution ◽  
Upper Bound ◽  
Brain Size ◽  
Network Approach ◽  
Cognitive Constraints ◽  
Hominin Evolution ◽  
Over Time ◽  
Size And Structure

In both modern humans and non-human primates, time and cognitive constraints place an upper bound on the number of social relationships an individual can maintain at a given level of intensity. Similar constraints are likely to have operated throughout hominin evolution, shaping the size and structure of social networks. One of the key trends in human evolution, alongside an increase in brain size, is likely to have been an increase in group size, resulting in a larger number of social relationships that would have to be maintained over time. The network approach demonstrates that relationships should not be viewed as dyadic ties between two individuals, but as embedded within a larger network of ties between network members. Together with relationships based on kinship, this may have allowed for larger groups to be maintained among hominins than would be possible if such networks were based purely on dyadic ties between individuals.

Bridging Online and Offline Social Networks to Promote Health Innovation

2011 ◽  
pp. 462-480
Author(s):  
Cameron Norman
Keyword(s):  
Social Networks ◽  
Knowledge Translation ◽  
Social Engagement ◽  
Food Systems ◽  
Idea Generation ◽  
Building Relationships ◽  
Health Innovation ◽  
Face To Face ◽  
Over Time

Complex problems require strategies that leverage the knowledge of diverse actors working in a coordinated manner in order to address them in a manner that is appropriate to the context. Such strategies require building relationships among groups that enable them to network in ways that have the intensity of face-to-face meetings, but also extend over time. The Complexity, Networks, EHealth, & Knowledge Translation Research (CoNEKTR) model draws upon established methods of face-to-face social engagement and supported with information technology and proscribes an approach to issue exploration, idea generation and collective action that leverages social networks for health innovation. The model combines aspects of communities of practice, online communities, systems and complexity science, and theories of knowledge translation, exchange and integration. The process and steps of implementing the model are described using a case study applied to food systems and health. Implications for health research and knowledge translation are discussed.

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