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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 29-38    DOI: 10.11925/infotech.2096-3467.2019.0735
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Identifying Potential Trending Topics of Online Public Opinion
Ding Shengchun1,2(),Yu Fengyang1,Li Zhen1
1School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094, China
2Jiangsu Social Public Security Science and Technology Collaborative Innovation Center, Nanjing 210094, China
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Abstract  

[Objective] This paper tries to find potential trending topics from the online data, aiming to help government or enterprises monitor and guide public opinion.[Methods] First, we collected topics of public opinion with microblog’s real-time data stream. Then, we identified features of trending topics. Finally, we compared the performance of the Logistic Regression and SVM models for predicting potential trending topics.[Results] The Logistic Regression model is more capable of finding potential trending topics (recall=0.89) than SVM.[Limitations] More research is needed to examine our model with other social media platforms.[Conclusions] The proposed model could effectively identify potential trending topics of online public opinion.
Key words： Internet Public Opinion      Identification of Potential Hot Topics      Logistic Regression      Support Vector Machine     
Received: 24 June 2019      Published: 26 April 2020
ZTFLH:  TP391 N99  
Corresponding Authors: Ding Shengchun     E-mail: todingding@163.com
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Shengchun Ding
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Cite this article:

Ding Shengchun,Yu Fengyang,Li Zhen. Identifying Potential Trending Topics of Online Public Opinion. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 29-38.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0735     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I2/3/29

The Framework for Identifying Potential Hot Topics of Network Public Opinion
序号 特征量化
1 单位时间内主题相关微博增量
2 单位时间内主题相关微博的评论增量
3 单位时间内主题相关微博的转发增量
4 单位时间内主题相关微博的点赞增量
5 主题相关用户最近30天内的日均发博数
6 主题相关用户最近30天内的粉丝互动h指数
7 主题相关用户的高质量粉丝数
8 主题相关用户最近30天内的微博平均评论数
9 主题相关用户最近30天内的微博平均转发数
10 主题相关用户最近30天内的微博平均点赞数
Identification Characteristics of Potential Hot Topics
意见领袖所属类别 参考来源
政务类 微博榜单及2017年度人民日报·政务指数微博影响力报告[20]
传统媒体类(含报纸、杂志、媒体网站等) 微博榜单
互联网类
自媒体人气大V类
娱乐类
财经类 新浪全媒体影响力排行榜②(②http://blog.sina.com.cn/lm/bang/.)
Selection Categories and Reference Sources of Opinion Leaders
Execution Result of Subject Detection Program (Part)
Example of Topic Content Assignment for Three Categories of Rules
被过滤的主题类型 过滤原因
已登录微博热搜榜的主题 无预测价值
综合新闻或事件回顾 已失去时效性
交通、天气、股票等实时播报 日常或周期性事件,突发程度低,网络舆情监测价值或提前预警必要性较低
系列活动的日常报道
周期性事件
娱乐新闻、明星八卦 不属于本研究的目标服务群体
城市、图书、影视、音乐等推荐与分享 多数微博用户用于吸引粉丝的日常分享,不含较重大的社会事件或突发事件,监测价值较低
招聘启事、商业广告
人物访谈、人物简介、名人名言
搞笑段子、鸡汤文字
粉丝福利、日常互动
食谱、生活技巧、知识科普
便民提示、安全提醒
世界杯等体育赛事 该类事件属全民关注,极易登上微博热搜榜,提前预警的必要性低
Types and Reasons of Artificial Filtering of Public Opinion Topics
序号 特征 序号 特征
1 t1内的主题相关微博增量 17 t1内的主题相关用户粉丝互动h指数
2 t1内的主题相关微博的评论增量 18 t1内的主题相关用户高质量粉丝数
3 t1内的主题相关微博的转发增量 19 t1内的主题相关用户活跃度
4 t1内的主题相关微博的点赞增量 20 t1内的主题相关用户微博影响力
5 t2内的主题相关微博增量 21 t2内新增的主题相关用户粉丝互动h指数
6 t2内的主题相关微博的评论增量 22 t2内新增的主题相关用户高质量粉丝数
7 t2内的主题相关微博的转发增量 23 t2内新增的主题相关用户活跃度
8 t2内的主题相关微博的点赞增量 24 t2内新增的主题相关用户微博影响力
9 t3内的主题相关微博增量 25 t3内的主题相关用户粉丝互动h指数
10 t3内的主题相关微博的评论增量 26 t3内的主题相关用户高质量粉丝数
11 t3内的主题相关微博的转发增量 27 t3内的主题相关用户活跃度
12 t3内的主题相关微博的点赞增量 28 t3内的主题相关用户微博影响力
13 t4内的主题相关微博增量 29 t4内新增的主题相关用户粉丝互动h指数
14 t4内的主题相关微博的评论增量 30 t4内新增的主题相关用户高质量粉丝数
15 t4内的主题相关微博的转发增量 31 t4内新增的主题相关用户活跃度
16 t4内的主题相关微博的点赞增量 32 t4内新增的主题相关用户微博影响力
Potential Hot Topic Identification Feature Items
Example of Feature Item Extraction Results
Example of Manual Labeling Results
实验次数 Logistic Regression SVM
准确率 召回率 F1值 准确率 召回率 F1值
1 0.66 0.88 0.75 0.82 0.67 0.74
2 0.69 0.83 0.75 0.75 0.71 0.73
3 0.65 0.80 0.72 0.78 0.64 0.70
4 0.67 0.84 0.75 0.84 0.65 0.73
5 0.63 0.86 0.73 0.69 0.76 0.72
6 0.70 0.89 0.78 0.70 0.67 0.69
7 0.67 0.89 0.77 0.87 0.73 0.79
8 0.66 0.81 0.73 0.90 0.64 0.74
9 0.67 0.83 0.74 0.78 0.65 0.71
10 0.67 0.89 0.77 0.88 0.67 0.76
11 0.75 0.85 0.79 0.83 0.64 0.72
12 0.68 0.79 0.73 0.84 0.65 0.73
13 0.65 0.88 0.75 0.76 0.85 0.80
14 0.71 0.86 0.78 0.73 0.73 0.73
15 0.63 0.85 0.72 0.79 0.67 0.73
均值 0.67 0.85 0.75 0.80 0.69 0.73
Results of Potential Hot Topic Identification
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