新議題在異質性社會網路上的傳播預測

Novel Topic Information Diffusion Prediction on Heterogeneous Social Networks

洪三權(San-Chuan Hung)

指導教授 ： 林守德

國立臺灣大學/電機資訊學院/資訊網路與多媒體研究所/碩士(2013年)

https://doi.org/10.6342/NTU.2013.02477

摘要

新議題的傳播預測是社會網路分析領域新興且重要的問題。新議題由於缺乏過去的傳遞記錄，使得訊息的傳遞模式變得更加難以預測。相較於過去的研究，我們提出更真實的實驗情境；此外，我們運用議題語義相似性，並結合異質性社會網路的資訊，提出新的特徵值設計供機械學習的模型使用。根據實驗結果，結合新模型與過去舊有的模型，在評鑑指標：「接收者操作特徵曲線下面積」有3.51%的增進。

關鍵字

訊息傳遞 ； 新議題 ； 異質性社會網路 ； 社會網路分析

並列摘要

This work brings a marriage of two seemly unrelated topics, natural language processing (NLP) and social network analysis (SNA). Information diffusion prediction on novel topic is a challenging and important task in SNA, and we design a learning-based framework to solve this problem. Comparing to related work, we open the scenario into a more realistic setting, and exploit the latent semantic information among users, topics, social connections, and heterogeneous social network information as features for prediction. Our framework is evaluated on real data collected from public domain. The experiments show 3.51% AUC enhancement from baseline methods.

並列關鍵字

Information Diffusion ； Novel Topic ； Heterogeneous Social Networks ； Social Network Analysis

參考文獻

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