It, from the perspective of cohesion, extracts three types of topic-shift markers used in this genre, namely 'change of narrators', 'change of objects being talked about', and 'temporal adverbials&...It, from the perspective of cohesion, extracts three types of topic-shift markers used in this genre, namely 'change of narrators', 'change of objects being talked about', and 'temporal adverbials' to analyze four journalistic reports from the network media and draws the conclusion that cohesion between two topic units is usually weaker than that within a specific topic fragment.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It, from the perspective of cohesion, extracts three types of topic-shift markers used in this genre, namely 'change of narrators', 'change of objects being talked about', and 'temporal adverbials' to analyze four journalistic reports from the network media and draws the conclusion that cohesion between two topic units is usually weaker than that within a specific topic fragment.
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.
