Social information sharing in digital gaming on social network platforms through open standards

Author(s):  
Fabrizio Davide ◽  
Francesco Collova ◽  
F. Vatalaro
2021 ◽  
Author(s):  
◽  
Daniel Donoghue

<p>Social learning and network analyses are theorised to be of great utility in the context of behavioural conservation. For example, harnessing a species’ capacity for social learning may allow researchers to seed useful information into populations, while network analyses could provide a useful tool to monitor community stability, and predict pathways of pathogen transfer. Thus, an understanding of how individuals learn and the nature of the social networks within a population could enable the development of new behavioural based conservation interventions for species facing rapid environmental change, such as human-induced habitat modification. Parrots, the most threatened avian order worldwide, are notably underrepresented in the social learning and social network literature. This thesis addresses this knowledge gap by exploring social learning and networks using two endangered species of parrot; kākā (Nestor meridionalis) and kea (Nestor notabilis). The first study explores social learning of tool use in captive kea, using a trained kea demonstrator. The results from this experiment indicate that both social learning and play behaviour facilitated the uptake of tool use, and suggests that kea are highly sensitive to social information even when presented with complex tasks. The second study assesses whether wild kākā can socially learn novel string-pulling and food aversion behaviours from video playbacks of conspecific demonstrators. Although there was no evidence to indicate that kākā learn socially, these individuals also show no notable reaction to video playback of a familiar predator. Therefore, these results are likely due to difficulties in interpreting information on the screens, and not necessarily a reflection of their ability to perceive social information. In the final study, social network analysis (SNA) was performed to map social connectivity within wellington’s urban kākā population. SNA indicates that kākā form non-random social bonds, selectively associating with some individuals more than others, and also show high levels of dissimilarity in community composition at different feeding sites. Taken together, these results provide rare empirical evidence of social learning in a parrot species and suggest that even complicated seeded behaviours can quickly spread to other individuals. These findings may also be indicative of the difficulties in conducting video playback experiments in wild conditions, which is an area in need of future research. Overall, these findings contribute to the very limited body of research on social learning and networks in parrots, and provide information of potential value in the management of these species.</p>


2021 ◽  
Author(s):  
Daniel Donoghue

<p>Social learning and network analyses are theorised to be of great utility in the context of behavioural conservation. For example, harnessing a species’ capacity for social learning may allow researchers to seed useful information into populations, while network analyses could provide a useful tool to monitor community stability, and predict pathways of pathogen transfer. Thus, an understanding of how individuals learn and the nature of the social networks within a population could enable the development of new behavioural based conservation interventions for species facing rapid environmental change, such as human-induced habitat modification. Parrots, the most threatened avian order worldwide, are notably underrepresented in the social learning and social network literature. This thesis addresses this knowledge gap by exploring social learning and networks using two endangered species of parrot; kākā (Nestor meridionalis) and kea (Nestor notabilis). The first study explores social learning of tool use in captive kea, using a trained kea demonstrator. The results from this experiment indicate that both social learning and play behaviour facilitated the uptake of tool use, and suggests that kea are highly sensitive to social information even when presented with complex tasks. The second study assesses whether wild kākā can socially learn novel string-pulling and food aversion behaviours from video playbacks of conspecific demonstrators. Although there was no evidence to indicate that kākā learn socially, these individuals also show no notable reaction to video playback of a familiar predator. Therefore, these results are likely due to difficulties in interpreting information on the screens, and not necessarily a reflection of their ability to perceive social information. In the final study, social network analysis (SNA) was performed to map social connectivity within wellington’s urban kākā population. SNA indicates that kākā form non-random social bonds, selectively associating with some individuals more than others, and also show high levels of dissimilarity in community composition at different feeding sites. Taken together, these results provide rare empirical evidence of social learning in a parrot species and suggest that even complicated seeded behaviours can quickly spread to other individuals. These findings may also be indicative of the difficulties in conducting video playback experiments in wild conditions, which is an area in need of future research. Overall, these findings contribute to the very limited body of research on social learning and networks in parrots, and provide information of potential value in the management of these species.</p>


2013 ◽  
Vol 411-414 ◽  
pp. 411-414
Author(s):  
Jing Bo Yuan ◽  
Bai Rong Wang ◽  
Ji Hao Yang ◽  
Shun Li Ding

As a social network, microblog has obtained great attention and gotten wide application. Applications of microblog need to retrieve quickly information with the support of real-time search technology in order to implement information sharing. A query classification algorithm of microblog for real-time search was put forward. Based on question classification mechanism, the algorithm divides queries into two categories: the candidate queries and the popular queries, and takes separate storage strategy. Test results show that the classification algorithm can reduce real-time search time and improve the efficiency of retrieval.


2020 ◽  
pp. 1-5
Author(s):  
Jingsong Zhao ◽  
Colleen M. McBride ◽  
Yue Guan

<b><i>Purpose:</i></b> In this brief report, we ask whether women’s interpretation of breast cancer risk based on their low likelihood of carrying a <i>BRCA1/2</i> mutation is associated with their information-sharing behavior, and whether misinterpretation is associated with motives for sharing the result. <b><i>Methods:</i></b> Women in mammography clinics who completed a brief family history assessment and deemed to be at low likelihood of carrying a <i>BRCA1/2</i> mutation were asked to complete a 1-time online survey between June 2016 and January 2017. <b><i>Results:</i></b> One-third (44/148) of women shared their family history screen result with someone in their social network. Result information was shared largely with a first-degree female relative to express feelings of relief (77%, 33/43). There were no differences in likelihood of sharing based on breast cancer risk interpretation. However, women who misinterpreted the implications of the result for general breast cancer risk reported more motives to share the result with their social network than those who accurately interpreted their breast cancer risk. <b><i>Conclusions:</i></b> As family history-based screening for hereditary breast cancer is broadly implemented, the communication needs of the majority of women who will be unlikely of carrying a <i>BRCA1/2</i> mutation must be considered. The motives of women who misinterpreted the implications of this result for breast cancer risk suggest the possibility that miscommunication could be spread to the broader family network.


2010 ◽  
Vol 3 (4) ◽  
pp. 454-471 ◽  
Author(s):  
Marion E. Hambrick ◽  
Jason M. Simmons ◽  
Greg P. Greenhalgh ◽  
T. Christopher Greenwell

The online social network Twitter has grown exponentially since 2008. The current study examined Twitter use among professional athletes who use Twitter to communicate with fans and other players. The study used content analysis to place 1,962 tweets by professional athletes into one of six categories: interactivity, diversion, information sharing, content, promotional, and fanship. Many of the tweets fell into the interactivity category (34%). Athletes used Twitter to converse directly with their followers. Those with the most followers had more interactivity tweets. A large percentage of tweets (28%) fell into the diversion category, because many of the tweets involved non-sports-related topics, and relatively few of the tweets (15%) involved players discussing their own teams or sports. In addition, only 5% of the tweets were promotional in nature, indicating that professional athletes may not be taking advantage of the promotional opportunities Twitter may provide.


2021 ◽  
Vol 17 (2) ◽  
pp. 1-16
Author(s):  
Abrar Al-Hasan

This study examines the value and impact of social network information on a user's language learning performance by conducting an online experiment in a peer-to-peer collaborative language learning marketplace. Social information or information about others in one's network can present a socially networked learning environment that enables learners to engage more in the learning process. Experimental research design in an online language learning marketplace was conducted. The study finds evidence that the mere visibility of social network information positively impacts a learner's learning performance. Learners that engage with social interaction perform better than those that do not. In addition, active social interaction has a stronger impact on learning performance as compared to passive social interaction. The study concludes with implications for platform developers to enable the visibility of social information and engineer the user experience to enhance interactive learning.


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