The social trend of thought is the trend of thought that is of extensive influence formed in a certain period of time in a given society. It o-riginates from the changes in international environment and domestic socia...The social trend of thought is the trend of thought that is of extensive influence formed in a certain period of time in a given society. It o-riginates from the changes in international environment and domestic social situation, reflecting the interests and demands of a given group of people and producing a great influence on the foreign and domestic policies and the future trend of the society. One prominent feature of展开更多
"Like tidal waves, the social trend of thought comes from surging public thinking echoing each other in turbulence along the same direction as a result of changes in environment and national mood in a civilized c..."Like tidal waves, the social trend of thought comes from surging public thinking echoing each other in turbulence along the same direction as a result of changes in environment and national mood in a civilized country", wrote Liang Qichao (1873-1929), a Chinese scholar and one of the leaders of展开更多
Purpose： We propose and apply a simplified nowcasting model to understand the correlations between social attention and topic trends of scientific publications. Design/methodology/approach： First, topics are generat...Purpose： We propose and apply a simplified nowcasting model to understand the correlations between social attention and topic trends of scientific publications. Design/methodology/approach： First, topics are generated from the obesity corpus by using the latent Dirichlet allocation （LDA） algorithm and time series of keyword search trends in Google Trends are obtained. We then establish the structural time series model using data from January 2004 to December 2012, and evaluate the model using data from January 2013. We employ a state-space model to separate different non-regression components in an observational time series （i.e. the tendency and the seasonality） and apply the ＂spike and slab prior＂ and stepwise regression to analyze the correlations between the regression component and the social media attention. The two parts are combined using Markov-chain Monte Carlo sampling techniques to obtain our results. Findings： The results of our study show that （1） the number of publications on child obesity increases at a lower rate than that of diabetes publications; （2） the number of publication on a given topic may exhibit a relationship with the season or time of year; and （3） there exists a correlation between the number of publications on a given topic and its social media attention, i.e. the search frequency related to that topic as identified by Google Trends. We found that our model is also able to predict the number of publications related to a given topic.展开更多
Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote bo...Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.展开更多
文摘The social trend of thought is the trend of thought that is of extensive influence formed in a certain period of time in a given society. It o-riginates from the changes in international environment and domestic social situation, reflecting the interests and demands of a given group of people and producing a great influence on the foreign and domestic policies and the future trend of the society. One prominent feature of
文摘"Like tidal waves, the social trend of thought comes from surging public thinking echoing each other in turbulence along the same direction as a result of changes in environment and national mood in a civilized country", wrote Liang Qichao (1873-1929), a Chinese scholar and one of the leaders of
基金supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2012-2012S1A3A2033291)the Yonsei University Future-leading Research Initiative of 2014
文摘Purpose： We propose and apply a simplified nowcasting model to understand the correlations between social attention and topic trends of scientific publications. Design/methodology/approach： First, topics are generated from the obesity corpus by using the latent Dirichlet allocation （LDA） algorithm and time series of keyword search trends in Google Trends are obtained. We then establish the structural time series model using data from January 2004 to December 2012, and evaluate the model using data from January 2013. We employ a state-space model to separate different non-regression components in an observational time series （i.e. the tendency and the seasonality） and apply the ＂spike and slab prior＂ and stepwise regression to analyze the correlations between the regression component and the social media attention. The two parts are combined using Markov-chain Monte Carlo sampling techniques to obtain our results. Findings： The results of our study show that （1） the number of publications on child obesity increases at a lower rate than that of diabetes publications; （2） the number of publication on a given topic may exhibit a relationship with the season or time of year; and （3） there exists a correlation between the number of publications on a given topic and its social media attention, i.e. the search frequency related to that topic as identified by Google Trends. We found that our model is also able to predict the number of publications related to a given topic.
文摘Information networks where users join a network, publish their own content, and create links to other users are called Online Social Networks (OSNs). Nowadays, OSNs have become one of the major platforms to promote both new and viral applications as well as disseminate information. Social network analysis is the study of these information networks that leads to uncovering patterns of interaction among the entities. In this regard, finding influential users in OSNs is very important as they play a key role in the success above phenomena. Various approaches exist to detect influential users in OSNs, starting from simply counting the immediate neighbors to more complex machine-learning and message-passing techniques. In this paper, we review the recent existing research works that focused on identifying influential users in OSNs.
