Computational Social Networks最新文献

Contextual polarity and influence mining in online social networks 在线社交网络中的语境极性与影响挖掘

Computational Social Networks Pub Date : 2021-10-14 DOI: 10.1186/s40649-021-00101-3

Alzahrani, Hassan, Acharya, Subrata, Duverger, Philippe, Nguyen, Nam P.

{"title":"Contextual polarity and influence mining in online social networks","authors":"Alzahrani, Hassan, Acharya, Subrata, Duverger, Philippe, Nguyen, Nam P.","doi":"10.1186/s40649-021-00101-3","DOIUrl":"https://doi.org/10.1186/s40649-021-00101-3","url":null,"abstract":"Crowdsourcing is an emerging tool for collaboration and innovation platforms. Recently, crowdsourcing platforms have become a vital tool for firms to generate new ideas, especially large firms such as Dell, Microsoft, and Starbucks, Crowdsourcing provides firms with multiple advantages, notably, rapid solutions, cost savings, and a variety of novel ideas that represent the diversity inherent within a crowd. The literature on crowdsourcing is limited to empirical evidence of the advantage of crowdsourcing for businesses as an innovation strategy. In this study, Starbucks’ crowdsourcing platform, Ideas Starbucks, is examined, with three objectives: first, to determine crowdsourcing participants’ perception of the company by crowdsourcing participants when generating ideas on the platform. The second objective is to map users into a community structure to identify those more likely to produce ideas; the most promising users are grouped into the communities more likely to generate the best ideas. The third is to study the relationship between the users’ ideas’ sentiment scores and the frequency of discussions among crowdsourcing users. The results indicate that sentiment and emotion scores can be used to visualize the social interaction narrative over time. They also suggest that the fast greedy algorithm is the one best suited for community structure with a modularity on agreeable ideas of 0.53 and 8 significant communities using sentiment scores as edge weights. For disagreeable ideas, the modularity is 0.47 with 8 significant communities without edge weights. There is also a statistically significant quadratic relationship between the sentiments scores and the number of conversations between users.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"429 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fairness norm through social networks: a simulation approach 社会网络中的公平规范:模拟方法

Computational Social Networks Pub Date : 2021-10-09 DOI: 10.1186/s40649-021-00100-4

Omar Rifki, Hirotaka Ono

引用次数: 0

Understanding how retweets influence the behaviors of social networking service users via agent-based simulation 通过基于代理的模拟了解转发如何影响社交网络服务用户的行为

Computational Social Networks Pub Date : 2021-09-13 DOI: 10.1186/s40649-021-00099-8

Yan, Yizhou, Toriumi, Fujio, Sugawara, Toshiharu

{"title":"Understanding how retweets influence the behaviors of social networking service users via agent-based simulation","authors":"Yan, Yizhou, Toriumi, Fujio, Sugawara, Toshiharu","doi":"10.1186/s40649-021-00099-8","DOIUrl":"https://doi.org/10.1186/s40649-021-00099-8","url":null,"abstract":"The retweet is a characteristic mechanism of several social network services/social media, such as Facebook, Twitter, and Weibo. By retweeting tweet, users can share an article with their friends and followers. However, it is not clear how retweets affect the dominant behaviors of users. Therefore, this study investigates the impact of retweets on the behavior of social media users from the perspective of networked game theory, and how the existence of the retweet mechanism in social media promotes or reduces the willingness of users to post and comment on articles. To address these issues, we propose the retweet reward game model and quote tweet reward game model by adding the retweet and quote tweet mechanisms to a relatively simple social networking service model known as the reward game. Subsequently, we conduct simulation-based experiments to understand the influence of retweets on the user behavior on various networks. It is demonstrated that users will be more willing to post new articles with a retweet mechanism, and quote retweets are more beneficial to users, as users can expect to spread their information and their own comments on already posted articles.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"49 1-2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A robust optimization model for influence maximization in social networks with heterogeneous nodes 异构节点社交网络中影响力最大化的鲁棒优化模型

Computational Social Networks Pub Date : 2021-08-27 DOI: 10.1186/s40649-021-00096-x

Agha Mohammad Ali Kermani, Mehrdad, Ghesmati, Reza, Pishvaee, Mir Saman

引用次数: 1

Celebrity profiling through linguistic analysis of digital social networks 通过数字社交网络的语言分析来分析名人

Computational Social Networks Pub Date : 2021-08-26 DOI: 10.1186/s40649-021-00097-w

Moreno-Sandoval, Luis G., Pomares-Quimbaya, Alexandra, Alvarado-Valencia, Jorge A.

{"title":"Celebrity profiling through linguistic analysis of digital social networks","authors":"Moreno-Sandoval, Luis G., Pomares-Quimbaya, Alexandra, Alvarado-Valencia, Jorge A.","doi":"10.1186/s40649-021-00097-w","DOIUrl":"https://doi.org/10.1186/s40649-021-00097-w","url":null,"abstract":"Digital social networks have become an essential source of information because celebrities use them to share their opinions, ideas, thoughts, and feelings. This makes digital social networks one of the preferred means for celebrities to promote themselves and attract new followers. This paper proposes a model of feature selection for the classification of celebrities profiles based on their use of a digital social network Twitter. The model includes the analysis of lexical, syntactic, symbolic, participation, and complementary information features of the posts of celebrities to estimate, based on these, their demographic and influence characteristics. The classification with these new features has an F1-score of 0.65 in Fame, 0.88 in Gender, 0.37 in Birth year, and 0.57 in Occupation. With these new features, the average accuracy improve up to 0.14 more. As a result, extracted features from linguistic cues improved the performance of predictive models of Fame and Gender and facilitate explanations of the model results. Particularly, the use of the third person singular was highly predictive in the model of Fame.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"93 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138542871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Modelling community structure and temporal spreading on complex networks 复杂网络上群落结构和时间扩展的建模

Computational Social Networks Pub Date : 2021-03-18 DOI: 10.1186/s40649-021-00094-z

Vesa Kuikka

引用次数: 2

Utilizing the simple graph convolutional neural network as a model for simulating influence spread in networks 利用简单图卷积神经网络作为模型来模拟网络中的影响传播

Computational Social Networks Pub Date : 2021-03-17 DOI: 10.1186/s40649-021-00095-y

Alexander V. Mantzaris, Douglas Chiodini, Kyle Ricketson

{"title":"Utilizing the simple graph convolutional neural network as a model for simulating influence spread in networks","authors":"Alexander V. Mantzaris, Douglas Chiodini, Kyle Ricketson","doi":"10.1186/s40649-021-00095-y","DOIUrl":"https://doi.org/10.1186/s40649-021-00095-y","url":null,"abstract":"The ability for people and organizations to connect in the digital age has allowed the growth of networks that cover an increasing proportion of human interactions. The research community investigating networks asks a range of questions such as which participants are most central, and which community label to apply to each member. This paper deals with the question on how to label nodes based on the features (attributes) they contain, and then how to model the changes in the label assignments based on the influence they produce and receive in their networked neighborhood. The methodological approach applies the simple graph convolutional neural network in a novel setting. Primarily that it can be used not only for label classification, but also for modeling the spread of the influence of nodes in the neighborhoods based on the length of the walks considered. This is done by noticing a common feature in the formulations in methods that describe information diffusion which rely upon adjacency matrix powers and that of graph neural networks. Examples are provided to demonstrate the ability for this model to aggregate feature information from nodes based on a parameter regulating the range of node influence which can simulate a process of exchanges in a manner which bypasses computationally intensive stochastic simulations.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"48 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Modeling and analyzing users’ behavioral strategies with co-evolutionary process 基于协同进化过程的用户行为策略建模与分析

Computational Social Networks Pub Date : 2021-03-10 DOI: 10.1186/s40649-021-00092-1

Yutaro Miura, Fujio Toriumi, Toshiharu Sugawara

{"title":"Modeling and analyzing users’ behavioral strategies with co-evolutionary process","authors":"Yutaro Miura, Fujio Toriumi, Toshiharu Sugawara","doi":"10.1186/s40649-021-00092-1","DOIUrl":"https://doi.org/10.1186/s40649-021-00092-1","url":null,"abstract":"Social networking services (SNSs) are constantly used by a large number of people with various motivations and intentions depending on their social relationships and purposes, and thus, resulting in diverse strategies of posting/consuming content on SNSs. Therefore, it is important to understand the differences of the individual strategies depending on their network locations and surroundings. For this purpose, by using a game-theoretical model of users called agents and proposing a co-evolutionary algorithm called multiple-world genetic algorithm to evolve diverse strategy for each user, we investigated the differences in individual strategies and compared the results in artificial networks and those of the Facebook ego network. From our experiments, we found that agents did not select the free rider strategy, which means that just reading the articles and comments posted by other users, in the Facebook network, although this strategy is usually cost-effective and usually appeared in the artificial networks. We also found that the agents who mainly comment on posted articles/comments and rarely post their own articles appear in the Facebook network but do not appear in the connecting nearest-neighbor networks, although we think that this kind of user actually exists in real-world SNSs. Our experimental simulation also revealed that the number of friends was a crucial factor to identify users’ strategies on SNSs through the analysis of the impact of the differences in the reward for a comment on various ego networks.","PeriodicalId":52145,"journal":{"name":"Computational Social Networks","volume":"14 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138524845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Network analysis of internal migration in Croatia 克罗地亚国内移徙的网络分析

Computational Social Networks Pub Date : 2021-03-04 DOI: 10.1186/s40649-021-00093-0

Dino Pitoski, Thomas J. Lampoltshammer, P. Parycek

引用次数: 2

Influence maximization in social media networks concerning dynamic user behaviors via reinforcement learning 通过强化学习实现动态用户行为在社交媒体网络中的影响最大化

Computational Social Networks Pub Date : 2021-02-22 DOI: 10.1186/s40649-021-00090-3

Mengnan Chen, Q. Zheng, V. Boginski, E. Pasiliao

引用次数: 0