【目的】分析并評述當前在網絡上監測極端主義思想傳播和個體思想激進化的主要技術。【方法】在文獻分析的基礎上,對相關技術的解決思路、數據來源、標注方法、算法進行分析歸納。【結果】在社交網絡極端主義檢測與發現領域,研究者更多地借鑒心理學和社會學研究成果,細化檢測指標和檢測方法,構造多樣化的檢測模式。主要技術分為基于詞典和基于機器學習兩類,由于基于機器學習的方法具有準確率高、速度快的優勢,因此使用較基于詞典的方法更加頻繁,但是如何科學有效地標注訓練數據集是研究難點。【結論】該領域的技術應用尚處于初級探索階段,需要將更多的量化研究投入到對激進化過程的分析中。檢測技術研究者應更多地與社會學和心理學研究者合作,以開發出更加精細的模型。需要投入更多的研究資源以提高訓練數據集的標注速度和準確性。

[Objective] This paper reviews the technical solutions for detecting online extremism and radicalization.[Methods] First, we retrieved the needed literature by conducting keyword search with several popular academic databases. Then, we reviewed these papers and summarized their theoretical frameworks, data sources, labelling method, and algorithms. [Results] Researchers have obtained insights from the latest psychology and sociology studies, which helped them refine the detection indicators and methods. The two popular techniques used in this field were based on lexicon method and machine learning algorithm. Although machine-learning methods had the advantages of better accuracy and faster speed, it is very hard for us to construct the training data sets. [Limitations] We did not compare the effectiveness of different solutions. [Conclusions] The reviewed techniques are still developing and more quantitative research is required to analyze the radicalization process. We need to co-operate with sociology and psychology researchers to develop new models and better training data sets.