► Cooperation among prominent actors in a tourist community is relation-based. ► Preferences to cooperate are more relevant at personal than institutional level. ► Information exchange occurs but ...reflects a formal rule (social exchange theory). ► Cooperative behavior is linked to sympathy (behavioral game theory). ► Local social networks and personal bonds increase the likelihood of cooperation.
Cooperative behavior in tourism destination communities is a condition for sustainable planning and development. However, evidence is lacking on how actors choose to cooperate. Previous research in institutions, organizations, and communities show that formal, contract-based as well as informal, relation-based cooperation occur jointly or in substitution, depending on the context and the subject of research. However, neither the approaches nor their underlying dimensions have been tested for the reality of tourist destination communities. For a European Alpine tourism destination the results show that only relation-based items, in combination with communication variables, strongly positively influence cooperative behavior. The paper suggests a series of implications for tourism destination planning and concludes with indications for further research.
Social information, acquired through the observation of others, has been documented in a variety of adaptive contexts. The transmission of social information relies on social connections and ...therefore it is important to consider that individuals may vary in their access to, and use of, such information. Social network analysis allows for the consideration of individual variation in social connections, which until recently has been ignored in the study of social processes. Furthermore, few previous studies of social information use have considered the potential effects of traits such as dominance and personality, which have been found to influence group social structure. We used network-based diffusion analysis, which incorporates information on individual social associations, to examine whether wild flocks of black-capped chickadees, Poecile atricapillus, utilize social information when locating novel foraging patches. Additionally, we incorporated individual traits (age, sex, dominance and exploratory personality) while examining flocks from rural and urban environments, to assess the influence of individual and habitat level characteristics on the rate of information transmission. Social information transmission was found to occur in all flocks, as individual time of discovery of the novel foraging patches was explained by network connections as predicted. However, the only individual level variable found to influence social transmission was dominance rank: dominant individuals had higher rates of information transmission than subordinates. We also found that the rate of social information transmission was higher in rural than urban environments. Our results highlight the importance of considering social associations when examining social information use. Additionally, our results suggest that dominant individuals have greater access to social information than more subordinate individuals, which may demonstrate a previously undocumented additional benefit provided by social dominance.
•Few studies examine individual and ecological drivers of social information use.•Foraging information is transmitted socially through wild chickadee flock networks.•Dominant individuals have an increased rate of acquiring social information.•Social transmission rate was higher in flocks from rural rather than urban sites.
As a traditional agricultural country, China has always prioritized agricultural development, and has increasingly focused on green and sustainable agricultural development. Based on the ...inter-provincial panel data for China from 1997 to 2019, this study divided these data into five periods according to the Five-Year Plan (FYP) of China, measured the agricultural eco-efficiency (AEE) values using the Super-SBM model, and then determined the spatial association network of the inter-provincial AEE of China using the improved gravity model. Finally, social network analysis (SNA) was used to further analyze the evolution process of AEE, and we developed a framework of how multidimensional proximity, which includes geographical, economic, technological, cognitive, and institutional proximity, made an influence on the formation of AEE spatial relation network. The findings indicated that: 1) in 1997–2019, the AEE in China was present in some spatial and temporal differences characteristics at the provincial scale, and we specifically found that national macro-regulation and policy incentives played a positive role in the long-term development of AEE. 2) The spatial correlation of AEE development among provincial regions were becoming closer and exhibits obvious spatial correlation and spillover effects. The evolution of the AEE network has clearly observable trends of hierarchization and aggregation, and the complexity of the correlation network continues to increase and exhibits spatial clustering characteristics that are dense in the east and sparse in the west. The network structure has changed from monocentric radiation to a multicentric network, and network nodes select the more advantageous nodes with which to connect. 3) Finally, the geographical proximity had a significant negative effect; the economic, technological, and institutional proximities were all observed to contribute to the AEE network formation, and cognitive proximity did not significantly influence this network formation.
The optimization of cultural communication paths is an important part of Chinese culture’s continued dialogue with the world in the face of increasingly fierce international competition. In this ...paper, we use social network analysis to collect relevant data on the microblogging platform and analyze the overall network structure of “The Belt and Road Initiative” using network density, network distance, and clustering coefficients. Using social network analysis metrics, we calculate the node position of the network path of “The Belt and Road Initiative” and analyze the influence of each microblog account. We filter the cultural communication topics of “The Belt and Road Initiative”, scrutinize the participating regions, and establish the characteristics of the cooperation mode within this cultural communication. “The Belt and Road Initiative” has a network density of 0.247, 0.281, and 0.295, respectively, which indicates that the network density of these three topics is small and there is less information exchange, but the cohesive force of cultural communication is stronger. “People’s Daily,” “Central Committee of the Communist Youth League,” and “Phoenix Video” have high intermediary centrality (712.256, 700.425, and 695.253, respectively). The high intermediary centrality indicates that these nodes have a great influence on the flow of resources in the information dissemination network and can accelerate the efficiency of the “Belt and Road Initiative” cultural dissemination path.
This paper constructs a GCN-based animation IP propagation algorithm based on social network analysis. Through the analysis of social network influence, a topology-based social network influence ...model is constructed. Combined with the ranking algorithm, the influence size of animation IP nodes is judged. The LDA model was used to construct a huge document set with the character IPs in the animation as documents. The clustering algorithm is used to classify the propagation effect of animation IP nodes, and the interval of animation IP propagation maximization is found by combining the submodular function maximization. The feature extraction of animation IP propagation effect maximization is performed by graph convolutional neural network. Evaluation indexes are constructed to assess the spreading ability of animation IP under social networks. The results show that the spreading power of animation IP with negative celebrity endorsement is −0.1, and the spreading effect of animation IP with positive communication content is 0.6.
In "Social Networks and Migration," Louise RYAN reexamines data obtained over 15 years of research to explore how migrants of different ages and backgrounds construct their social networks and how ...these networks evolve with time as migrants go through different stages in life. RYAN explores the aspirations and needs which migrants aim to address by nurturing existing connections or by creating new ones. With RYAN's approach to qualitative social network analysis (SNA) she emphasizes the specificities—i.e., the content and meaning—of each social connection. RYAN focuses on the individuality of each experience, and intentionally eschews the temptation to generalize to groups or identities of any kind. Her data are based on narratives ("telling network stories"), which yield information on relationships, on their meaning to interviewees, and on their nature and content. The book is particularly insightful in describing the content of relationships as well as their evolution over time. It also inevitably leaves out of its field of investigation more than it covers. It is therefore an invitation to further research into the way migrants shape their networks, how social networks shape their lives, and how they distil this experience in the form of narratives.
Tourist flow research is an important part of tourism research, providing the basis for the development of tourism. This paper takes different scenic spots in nine regions of M as the research ...object, takes social network analysis as the primary research method, and evaluates the node and overall network structure characteristics of tourist flow in M through the social node and overall network structure indicators. Using the “Octopus Collector” software to collect data, integrating 685 online travelogues about this region on the platform of related tourism websites, and using related software to process and analyze the data, it is found that there is a great deal of variability between different tourist attractions in M. From the viewpoint of node network structure, M1 scenic spot is in the center position between M tourist attractions, and among the 38 different scenic spots investigated in M area, there are only 6 attractions with extremely strong competitiveness, which can play the role of guiding the flow direction of tourist streams, and the others basically rely on the driving of the tourist volume of these tourist nodes in order to develop. From the overall network structure, the outward value, inward value, outward value and inward value close to the center potential of the degree center potential in the tourist flow network of M is greater than 30%, the overall scenic nodes of M are not closely connected, and the difference between the core scenic spots and the marginal scenic spots in terms of tourist flow is large.
•We consider the invisible co-authorship network among Process Mining researchers.•The features of the networks are compared in the two first decades of the 21st century.•We examine the scale-free ...feature and small-world characteristics in this network.•We used TOPSIS in order to integrate measures to identify the most central authors.•Network without central authors and without nodes less than 3 degrees is examined.
As a noticeable focal point in the field of analysis tools, Social Network Analysis (SNA) has received much interest to model real-world phenomena in a variety of domains, e.g., research collaboration and have a better perception of social events. Research collaboration refers to the main procedure of integrating disorganized capabilities and knowledge into novel research techniques and ideas. The invaluable analytical indicator of research collaboration is the outcome of analyses of scientific articles developed as its achievements. This article investigates collaboration and co-authorship in the Process Mining field based on the already published dataset consisting of 1278 papers which are selected by their keywords or snowball technique. According to crucial results, the co-authorship network developed among researchers features a number of the properties of the scale-free networks. Additionally, using mathematics, it has been proven that the acquired network is small world network. Besides, most central authors are determined by integrating four centrality measures include closeness, degree, eigenvector, and betweenness via TOPSIS. This network has been compared and reviewed in the absence/presence of such actors. In accordance with the obtained affiliation of the high-ranking authors, TU/e university plays the most pivotal role in Process Mining promotion.
Major depressive disorder (MDD) or depression is among the most prevalent psychiatric disorders, affecting more than 300 million people globally. Early detection is critical for rapid intervention, ...which can potentially reduce the escalation of the disorder.
This study used data from social media networks to explore various methods of early detection of MDDs based on machine learning. We performed a thorough analysis of the dataset to characterize the subjects' behavior based on different aspects of their writings: textual spreading, time gap, and time span.
We proposed 2 different approaches based on machine learning singleton and dual. The former uses 1 random forest (RF) classifier with 2 threshold functions, whereas the latter uses 2 independent RF classifiers, one to detect depressed subjects and another to identify nondepressed individuals. In both cases, features are defined from textual, semantic, and writing similarities.
The evaluation follows a time-aware approach that rewards early detections and penalizes late detections. The results show how a dual model performs significantly better than the singleton model and is able to improve current state-of-the-art detection models by more than 10%.
Given the results, we consider that this study can help in the development of new solutions to deal with the early detection of depression on social networks.
The growth and development of the internet users have given Indonesia an opportunity to develop internet-based services, such as online travel agents (OTA). Along with this OTA development, ...conventional travel agents were declining. Many conventional travel agents have decided to switch to online travel agents. The emergence of new OTAs has also made OTAs competition more challenging. Thus, a lesson learned from the market leader OTA is expected to help new OTAs surviving the competition. This research uses the sentiment analysis method to understand consumers' perceptions towards OTA and uses the social network analysis method to recognize actors who play significant roles in the travel agent business network. Lastly, the marketing strategies of the major and well-known OTAs perceived by online consumers was analyzed. Using the data collected from three major OTAs social media network (i.e., Traveloka, Tiket, and Booking), it was found that the general impression of consumers towards OTA is a positive sentiment. Furthermore, each key actor for each OTAs can be recognized. Lastly, marketing strategies can be proposed, namely by providing the complete product offerings, provide competitive price, creating special promos for consumers, promotion to be carried out on all social media using Bahasa Indonesia, and make the products offered available throughout Indonesia and can be used by everyone, especially travelers.
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