JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (1): 94-101.doi: 10.6040/j.issn.1671-9352.1.2018.136

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Topic tag popularity prediction based on multi-dimensional features

Xin-le WANG1,2(),Wen-feng YANG3,Hua-ming LIAO1,Yong-qing WANG1,Yue LIU1,Xiao-ming YU1,Xue-qi CHENG1   

  1. 1. CAS Key Laboratory of Network Data Science and Technology, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
    2. Graduate Universityof Chinese Academyof Sciences, Beijing 100049, China
    3. China Mobile (Suzhou) Software Technology Co. LTD, Suzhou 215000, Jiangsu, China
  • Received:2018-10-17 Online:2020-01-20 Published:2020-01-10

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Abstract:

The characteristics of user network structure information and topic tags such as sentiment and regionality are analyzed. A popularity prediction model that considers the user's fan network structure characteristics and the subject tag's own characteristics is proposed. Experiments have proved that the newly proposed feature is effective and has a high reference value for predicting the popularity of future topic tags.

Key words: topic tag, popularity prediction, multiple feature

CLC Number: 

  • TP391

Fig.1

Time frequency distribution of Hashtag"

Table 1

Weibo data"

属性 属性值
文件大小/GB 130
微博消息数量 2×108
Hashtag数量 5 923 117

Table 2

Hashtag train data"

属性 属性值
文件大小/GB 1.1
训练样本数 370 633
特征类型数量 7
微博特征维度 7
Hashtag特征维度 4
时间序列特征维度 30
区地域特征维度 8
省地域特征维度 35
主题特征维度 60
用户向量表示维度 100
用户自身特征维度 2

Table 3

Userfan network structure data"

属性 属性值
文件大小/GB 5.6
具有粉丝的用户数量 167 114
用户总量 2 529 073
用户粉丝连接边的数量 5.5×108

Table 4

Comparison of SVM, RF and xgboost results"

模型 MSE MAE MSLE Time/s
SVM 4 740 092.8 6.09 1.60 55 800
随机森林 1 492 180.4 5.25 1.09 12 647
xgboost 1 228 629.5 4.86 0.97 10 237

Table 5

Behaves of each feature on xgboost"

特征 MSE MAE MSLE
all 1 228 629.50 4.86 0.97
-time 1 482 928.60 5.1 1.12
-topic 1 386 303.30 5.84 1.15
-location 1 472 190.20 5.63 1.13
-user embedding 1 503 357.10 5.38 1.11
-user feature 1 514 726.90 6.26 1.21
-weibo feature 1 475 384.30 5.22 1.09
-hashtag feature 1 509 744.40 5.22 1.1

Table 6

The change of the index after removing some features"

特征 MAE MSLE
topic 20.10 18.10
location 15.80 16.00
User embedding 10.10 14.10
Hashtag feature 7.00 13.00

Fig.2

The propagation of a message over a user's network"

Table 7

Propagating details of topic tags"

用户ID编号 消息类型 消息内容
0 原发 晚安,王嘎一王嘉尔自作曲WOLO, #王嘉尔拜托了冰箱#王嘉尔透鲜滴星期天王嘉尔Jackson
1 转发 @浅蓝之季:#王嘉尔拜托了冰箱#还有英智和bambam
2 转发 #王嘉尔拜托了冰箱#Jackson真的适合这种风格呢,特别青春洋溢另外我看到了何哥哥有爱滴小眼神,一直盯着我嘉@irene_hhy:#拜托了冰箱MC王嘉尔#
3 转发 #王嘉尔拜托了冰箱#哈哈哈,多点真诚啊首掌!@边际未明:?我学生卡都掏出来了车呢车呢?马赛克啥玩意儿?@jacky王嘉尔-是WANG:哈哈看截图真的好萌啊
4 转发 #王嘉尔拜托了冰箱#冰箱家族@supercalifragilisticexpialido7:p2—黄老师和嘉嘉的糖…第一次吃,我嘎跟个贴心小棉袄似的

Table 8

The geographical characteristics of the topic tag"

地域 具体地区 Hashtag
海外 中国以外的其他国家 韩国水晶防晒喷雾,龟岛
东北三省 黑龙江,吉林,辽宁 二人转,冰雪大世界
西北 青海,新疆,宁夏,甘肃,陕西 羊肉泡馍,青海湖旅游攻略

Table 9

Thematic features change over time"

Hashtag Day-1 Day-2 Day-3
文化新闻
脱口秀		 优酷,分享,新闻,故事,沙和,文化,微笑,老师,客户,一起,看看 今波,贞观,李世民,孟姜女哭,土豆,文化,天子,新闻,故事,什么,中国,下载 今波,季-,朱由校,秘招,文化,新闻,故事,花絮,独家,明朝,皇帝,救国,个人,前世,八仙,今生,录制,爱好,中国
周杰伦中国
好声音		 冯小刚,声音,大赞,领悟力,宣传片,给力,奥特曼,才情,满分,电影,幽默,合作 声音,杰伦,我的,偶像,中国,一直,我家,歌词,只有,随便,超级,长大,忘记,出来,好听 叶湘伦,忽高,颜值,只服,周杰伦歌迷,后援会,声音,音乐,杰伦,拜拜,窒息,崩溃,中国,现场,赶往,录制
家族世代 信托,视野,观点,商界,全球,中外,大佬,产品, 18年,人物,所有,结果,默默无闻,帝国 行善,莫忘,订阅,微信,简史,任正非,信托,工具,观点,斗争,对方,制度,迷茫,政经,技术 美国通用,海尔,惊天,马云,王健林,订阅,微信, 5年, 56亿美金,沉寂,筹谋,背后,故事,年前,人物

Table 10

Xgboost features rank the first 30 dimensions in importance"

Rank Feature
1 时间序列特征
2 省地域特征-第十维
3 地区地域特征-第一维
4 微博长度
5 主题向量-第一维
6 消息类型
7 主题向量-第五维
8 是否包含URL
9 评论数
10 转发数
11 省地域特征-第二十维
12 主题向量-第十维
13 地区地域特征-第五维
14 用户向量-第四维
15 粉丝数
16 主题向量-第十二维
17 主题向量-第十五维
18 地区地域特征-第七维
19 用户向量-第十维
20 地区地域特征-第二维
21 朋友数
22 微博情感性
23 省地域特征-第三维
24 地区地域特征-第六维
25 用户向量-第三十四维
26 主题向量-第十九维
27 省地域特征-第九维
28 是否包含数字
29 用户向量-第五十二维
30 用户向量-第六十四维
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