Internet Users' Psychosocial Attention Prediction: Web Hot Topic Prediction Based on Adaptive AR Model

Tong, Hengqing; Liu, Yang; Peng, Hui; Tang, Jing

doi:10.1109/iccsit.2008.53

Cited by 7 publications

(3 citation statements)

References 2 publications

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“…There are some researches predicting hot topics in online forums or information retrieval systems, but they did not utilize the rich relations between users in social network. Tong et al [31] put forward an adaptive autoregressive model to predict hot topics in the near future based on the web browsing log information. Buckley [7] tried to improve the performance of information retrieval by predicting which topics work well with which approaches.…”

Section: Popularity Predictionmentioning

confidence: 99%

Predicting the popularity of topics based on user sentiment in microblogging websites

Wang

Ding

et al. 2017

J Intell Inf Syst

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Behavioral economics show us that emotions play an important role in individual behavior and decision-making. Does this also affect collective decision making in a community? Here we investigate whether the community sentiment energy of a topic is related to the spreading popularity of the topic. To compute the community sentiment energy of a topic, we first analyze the sentiment of a user on the key phrases of the topic based on the recent tweets of the user. Then we compute the total sentiment energy of all users in the community on the topic based on the Markov Random Field (MRF) model and graph entropy model. Experiments on two communities find the linear correlation between the community sentiment energy and the real spreading popularity of topics. Based on the finding, we proposed two models to predict the popularity of topics. Experimental results show the effectiveness of the two models and the helpful of sentiment in predicting the popularity of topics. Experiments also show that community sentiment affects collective decision making of spreading a topic or not in the community.

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Section: Popularity Predictionmentioning

confidence: 99%

Predicting the popularity of topics based on user sentiment in microblogging websites

Wang

Ding

et al. 2017

J Intell Inf Syst

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“…There have been many studies using NLP topic detection approaches. The Adaptive Auto Regression (AR) model based on the Recursive Weighted Least Square (RWLS) method is presented to capture the Internet users' psychosocial attention behavior on how 'hot' topics such as 'Olympic Games' grow on the Internet [25]. Topicconditioned First Story Detection (FSD) method in conjunction with a supervised learning algorithm [26] and Document Clustering [27] are used to identify the earliest report to a certain event in news articles.…”

Section: Related Work 21 Topic Detection and Predictionmentioning

confidence: 99%

Analyzing future communities in growing citation networks

Jung

Segev

2013

Proceedings of the 2013 International Workshop on Mining Unstructured Big Data Using Natural Language Processing

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Citation networks contain temporal information about what researchers are interested in at a certain time. A community in such a network is built around either a renowned researcher or a common research field; either way, analyzing how the community will change in the future will give insight into the research trend in the future. The paper proposes methods to analyze how communities change over time in the citation network graph without additional external information and based on node and link prediction and community detection. Different combinations of the proposed methods are also analyzed. Experiments show that the proposed methods can identify the changes in citation communities multiple years in the future with performance differing according to the analyzed time span. Furthermore, the method is shown to produce higher performance when analyzing communities to be disbanded and to be formed in the future.

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“…Hengqing Tong et al proposed an approach to predict hot topic used user's attention [2]. They predict hot topic by a method based on adaptive AutoRegession (AR).…”

Section: Previous Workmentioning

confidence: 99%

BBS Topic's Hotness Forecast Based on Back-Propagation Neural Network

Ding

2010

2010 International Conference on Web Information Systems and Mining

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Online hot topic detection is a significant research field in web data mining, which can help people make policy decision or benefit to people's daily life. Actually, in recent years more and more hot topics are arising from BBS, often referred as online forum. BBS provide a communication platform for people to discuss and express their views. It's obvious that forecasting the hotness topics on BBS is important and meaningful. In this paper we present an approach to predict the hotness of topics based on BPNN (Back-Propagation Neural Network). Text sentiment internet user's attention and hotspot relative have been considered to forecast the hotness of topics. At the last the experiment results over SINA reading forum show our approach is effective.

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Internet Users' Psychosocial Attention Prediction: Web Hot Topic Prediction Based on Adaptive AR Model

Cited by 7 publications

References 2 publications

Predicting the popularity of topics based on user sentiment in microblogging websites

Predicting the popularity of topics based on user sentiment in microblogging websites

Analyzing future communities in growing citation networks

BBS Topic's Hotness Forecast Based on Back-Propagation Neural Network

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