越览（132）——精读期刊论文的3案例分析（1）

摘要：This issue will introduce the case study of the intensively read replica paper "Crowd intelligence knowledge mining method based o

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《基于共词网络的群智知识挖掘方法

——在应急决策中应用》的

3案例分析（1）”。

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Today, the editor brings the

＂Yue Lan (132)：Intensive reading of the journal article

＇Crowd intelligence knowledge mining method

based on co-word network– application

in emergency decision-making’

3 Case study(1)".

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一、内容摘要（Summary of Content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍精读复刻论文《基于共词网络的群智知识挖掘方法——在应急决策中应用》的3案例分析（1）。

This issue will introduce the case study of the intensively read replica paper "Crowd intelligence knowledge mining method based on co-word network – application in emergency decision-making" in terms of mind maps, intensively read content, and knowledge supplementation.

二、思维导图（Mind mapping）

首先描述了新冠肺炎疫情（COVID-19）爆发后的应急应对，尤其是社会网络公众在疫情中的作用。疫情爆发后，党中央迅速制定方案，组织防控措施，但由于新冠肺炎的高传染性、隐蔽性和变异性，政府和专家组的力量难以应对危机的巨大冲击，因此需要动员更多的群体智慧。

First, the emergency response after the outbreak of the COVID-19 epidemic is described, especially the role of the social network public in the epidemic. After the outbreak, the Party Central Committee quickly formulated plans and organized prevention and control measures. However, due to the high infectiousness, concealment and variability of the COVID-19, the power of the government and the expert group was difficult to cope with the huge impact of the crisis, so more group wisdom needed to be mobilized.

本文以武汉新冠疫情为例，利用新浪微博数据分析社会网络公众在疫情中的群智作用。通过Python工具抓取了2020年1月15日至2月29日的微博数据，经过筛选，最终获得54601条有效数据。分析表明，社会网络公众的情感和态度在疫情应对中发挥了重要作用，为应急决策提供了宝贵的群体智慧。

Taking the Wuhan COVID-19 epidemic as an example, this paper uses Sina Weibo data to analyze the role of the group wisdom of the social network public in the epidemic. The Weibo data from January 15 to February 29, 2020 was captured by Python tools. After screening, 54,601 valid data were finally obtained. The analysis shows that the emotions and attitudes of the social network public played an important role in the response to the epidemic and provided valuable group wisdom for emergency decision-making.

（二）方法过程（Methods and procedures）

本文基于用户生成内容（UGCs）分析疫情期间的群智知识，主要分为以下步骤：

This article analyzes crowd intelligence knowledge during the epidemic based on user-generated content (UGCs), which is mainly divided into the following steps:

1. 数据预处理与特征表示：利用Python-Jieba进行分词处理，去除停用词，得到有效术语。通过TF-IDF矩阵表示特征，并筛选出476个关键词作为有效特征，用于后续分析。

1. Data preprocessing and feature representation: Use Python-Jieba to perform word segmentation, remove stop words, and obtain valid terms. Use the TF-IDF matrix to represent features, and select 476 keywords as valid features for subsequent analysis.

2. 共词网络构建：依据关键词共现关系，通过专家打分法设定交互行为权重，使用sigmoid映射函数计算UGCs的影响力。构建共词矩阵并进行网络模块化分析，确定最优的词对共现强度定义。

2. Co-word network construction: Based on the co-occurrence relationship of keywords, the interaction behavior weight is set through the expert scoring method, and the influence of UGCs is calculated using the sigmoid mapping function. A co-word matrix is constructed and a network modularization analysis is performed to determine the optimal definition of word pair co-occurrence intensity.

3. 社区检测与主题提取：使用Louvain算法对共词网络进行社区检测，并人工标注每个社区的主题。分析结果显示，网络关键词大多与疫情相关主题紧密关联。

3. Community detection and topic extraction: The Louvain algorithm was used to detect the community of the co-word network, and the topic of each community was manually labeled. The analysis results showed that most of the network keywords were closely related to the epidemic-related topics.

4. 群智知识价值测量：根据UGCs特征表示，标识关键词及其相关博文，并测量群智知识的价值。通过专家讨论，确定了四个应急决策考虑因素：人员伤亡、二次危机、社会影响和应急效果。

4. Crowd-source knowledge value measurement: Based on the UGCs feature representation, identify keywords and their related blog posts, and measure the value of crowd-source knowledge. Through expert discussion, four emergency decision-making considerations were identified: casualties, secondary crises, social impact, and emergency response effects.

5. 专家群体决策：专家群体对四个主题的权重进行协商，最终得出应急决策属性权重。根据方案评分和加权处理，最终选择了最佳应急方案。

5. Expert group decision: The expert group negotiated the weights of the four topics and finally came up with the weights of the emergency decision attributes. Based on the scheme scoring and weighted processing, the best emergency scheme was finally selected.

四、知识补充（Knowledge supplement）

Sigmoid 映射函数（又称为逻辑函数）是一种广泛使用的数学函数，特别在机器学习、神经网络、统计学和数据分析中有着重要应用。其公式如下：

The Sigmoid mapping function (also known as the logical function) is a widely used mathematical function, especially in machine learning, neural networks, statistics, and data analysis. Its formula is as follows:

Sigmoid 函数的特点如下：

The characteristics of the Sigmoid function are as follows:

输出范围：Sigmoid 函数的输出值始终在0 和 1 之间，表示一个概率值或某种强度。这一特性使得 Sigmoid 函数常用于处理需要归一化的任务，例如分类问题中的概率预测。

Output range: The output value of the Sigmoid function is always between 0 and 1, indicating a probability value or a certain intensity. This feature makes the Sigmoid function often used to handle tasks that require normalization, such as probability prediction in classification problems.

S型曲线：Sigmoid 函数的图像呈现一个 S 型曲线。对于非常小或非常大的x，输出趋近于 0 或 1；而当x 接近 0 时，输出接近 0.5。

S-shaped curve: The graph of the Sigmoid function shows an S-shaped curve. For very small or very large x, the output approaches 0 or 1; and when x is close to 0, the output is close to 0.5.

平滑的过渡：Sigmoid 函数是一个连续且平滑的函数，具有可微性。它没有跳跃或断点，可以对输入进行平滑映射。

Smooth transition: The Sigmoid function is a continuous and smooth function that is differentiable. It has no jumps or breakpoints and can smoothly map the input.

对称性：Sigmoid 函数关于x=0 对称。即如果输入 x 为负值，那么其输出也会接近于 0；如果输入是正值，输出则接近于 1。

Symmetry: The Sigmoid function is symmetric about x=0. That is, if the input x is negative, then its output will be close to 0; if the input is positive, then the output will be close to 1.

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