Ranking news events by influence decay and information fusion for media and users
Liang Kong, Shan Jiang, Rui Yan, Shize Xu, Yan Zhang · 2012
In many cases, people would like to read the news with great importance on the Internet. However, what users can grasp covers a very small part compared with the huge amount of news which never stops increasing. In this paper, we try to find what users are most likely to be interested in. We notice that media focus plays an essential role in distinguishing news topics and user attention is also an important factor. Therefore, we first propose five strategies which only exploit media focus to decide news influence impact. Then we provide three strategies to combine user attention with media focus. Meanwhile, we also take four types of interaction between user attention and media focus into consideration. To the best of our knowledge, this is the first work to establish different models for computing influence decay of news topics. Experiments show that better influence scores will be achieved by a decay algorithm based on Ebbinghaus forgetting curve and information fusion by considering interactions between user attention and media focus.