Research on Author Attribution Based on Core Topic

Xu Meng; Jing Xie; Chunwang Li

doi:10.13266/j.issn.2095-5472.2023.030

PDF(2249 KB)

Knowledge Management Forum ›› 2023, Vol. 8 ›› Issue (5) : 351-364. DOI: 10.13266/j.issn.2095-5472.2023.030

Research on Author Attribution Based on Core Topic

Xu Meng ¹^,² ,
Jing Xie ¹ ,
Chunwang Li ³

Author information +

History +

Abstract

[Purpose/Significance] The basic purpose of this study is to study the use of topic characteristics in author attribution of Chinese social media texts. Word2vec is used to supplement the topic model to obtain the deficiencies of topic characteristics. At the same time, strategies are further developed to identify and screen the core topics in the topic characteristics and optimize the use of topic characteristics. So as to improve the using effect of subject features in author attribution. [Methods/Process] The research first used the LDA topic model to extract the academic topics and social topics of the candidate authors, and then used word2vec to develop a merge screening strategy to identify and represent the core topics, and finally used N-gram features and similarity calculation to achieve author attribution. [Results/Conclusion] The experimental results show that the use of core topic characteristics has a positive effect on author attribution of social texts. Meanwhile, the strategy and application of core topic characteristics proposed in this study can also optimize the effect of the use of topic-features, and the highest recognition rate will reach 83% when it is combined with stylistic-features.

Key words

author attribution / topic characteristics / N-gram / scientific research author / social media text

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Xu Meng , Jing Xie , Chunwang Li. Research on Author Attribution Based on Core Topic[J]. Knowledge Management Forum. 2023, 8(5): 351-364 https://doi.org/10.13266/j.issn.2095-5472.2023.030

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	Kalgutkar V, Kaur R, Gonzalez H, et al. Code authorship attribution: methods and challenges[J]. ACM computing surveys (CSUR), 2019, 52(1): 1-36. Cited in this article [1]

[2]	Alrabaee S, Debbabi M, Wang L. CPA: accurate cross-platform binary authorship characterization using LDA[J]. IEEE transactions on information forensics and security, 2020(15): 3051-3066.

[3]	Maglogiannis I, Iliadis L, Pimenidis E. Artificial intelligence applications and innovations[J]. IFIP advances in information and communication technology, 2020(583):55-266. Cited in this article [1]

[4]	刘颖,肖天久.金庸与古龙小说计量风格学研究[J].清华大学学报(哲学社会科学版), 2014,29(5):135-147,179.(LIU Y, XIAO T J. A Study of the stylistics of Jin Yong and Gu Long novels[J].Journal of Tsinghua University(philosophy and social sciences),2014,29(5):135-147,179.) Cited in this article [1]

[5]	百度百科.主题[EB/OL]. [2023-04-05].https://baike.baidu.com/item/主题/2894698.(Baidu Encyclopedia. Topic[EB/OL]. [2023-04-05].https://baike.baidu.com/item/主题/2894698.) Cited in this article [1]

[6]	Mendenhall T C. The characteristic curves of composition[J].Science,1887(214S):237-246. Cited in this article [1]

[7]	Hoover D L. Another perspective on vocabulary richness[J].Computers and the humanities,2003(37):151-178. Cited in this article [1]

[8]	De Vel O, Anderson A, Corney M, et al. Mining e-mail content for author identification forensics[J]. ACM SIGMOD record,2001,30(4):55-64. Cited in this article [1]

[9]	Keselj V, Peng FC, Cercone N, et al. N-gram based author profiles for authorship attribution[EB/OL].[2023-04-05]. https://core.ac.uk/display/24680735 . Cited in this article [1]

[10]	祁瑞华,杨德礼,郭旭,等.基于多层面文体特征的博客作者身份识别研究[J].情报学报, 2015,34(6):628-634. (QI R H, YANG D L, GUO X, et al. Blogger identification based on multidimensional stylistic features[J].Journal of the China Society for Scientific and Technical Information, 2015,34(6):628-634.) Cited in this article [1]

[11]	祁瑞华,郭旭,刘彩虹.中文微博作者身份识别研究[J].情报学报,2017,36(1):72-78.(QI R H, GUO X, LIU C H. Authorship attribution of Chinese Microblog[J].Journal of the China Society for Scientific and Technical Information, 2017,36(1):72-78.) Cited in this article [1]

[12]	Finn A, Kushmerick N. Learning to classify documents according to genre[J]. Journal of the American Society for Information Science and Technology, 2006, 57(11): 1506-1518. Cited in this article [1]

[13]	Savoy J. Authorship attribution based on a probabilistic topic model[J]. Information processing & management, 2013, 49(1): 341-354. Cited in this article [2]

[14]	Anwar W, Bajwa I S, Choudhary M A, et al. An empirical study on forensic analysis of Urdu text using LDA-based authorship attribution[J]. IEEE access,2019(7): 3224-3234. Cited in this article [1]

[15]	Nie Y, Huang J, Li A, et al. Identifying users based on behavioral-modeling across social media sites[J].Web technologies and applications, 2014(8709):48-55. Cited in this article [1]

[16]	孙学刚,陈群秀,马亮.基于主题的Web文档聚类研究[J].中文信息学报,2003(3):21-26.(SUN X G,CHEN Q L,MA L. Study on topic-based web clustering[J].Journal of Chinese information processing,2003(3):21-26.) Cited in this article [1]

[17]	李湘东,张娇,袁满.基于LDA模型的科技期刊主题演化研究[J].情报杂志,2014,33(7):115-121.(LI X D, ZHANG J, YUAN M. On topic evolution of a scientific journal based on LDA model[J]. Journal of intelligence,2014,33(7):115-121.) Cited in this article [1]

[18]	陈思含.基于微博的多特征情感分析方法研究[D].长春:吉林大学,2021.(CHEN S H. Research on multi-feature sentiment analysis method based on microblog[D].Changchun: Jilin University,2021.) Cited in this article [1]

[19]	姚全珠,宋志理,彭程.基于LDA模型的文本分类研究[J].计算机工程与应用, 2011, 47(13): 150-153.(YAO Q Z,SONG Z L,PENG C. Research on text categorization based on LDA[J].Computer engineering and applications, 2011, 47(13): 150-153.) Cited in this article [1]

[20]	王振振,何明,杜永萍.基于LDA主题模型的文本相似度计算[J].计算机科学, 2013, 40(12):229-232.(WANG Z Z, HE M,DU Y P. Text similarity computing based on topic model LDA[J].Computer science, 2013, 40(12):229-232.) Cited in this article [1]

[21]	崔凯. 基于LDA的主题演化研究与实现[D]. 长沙: 国防科学技术大学,2010. Cited in this article [1]

(CUI K. The research and implementation of topic evolution based on LDA [D]. Changsha: National University of Defense Technology, 2010.)

[22]	马思丹,刘东苏.基于加权Word2vec的文本分类方法研究[J].情报科学,2019,37(11):38-42.(MA S D, LIU D S. Text classification method based on weighted word2vec [J]. Information science, 2019,37(11):38-42.) Cited in this article [1]

[23]	李晓,解辉,李立杰.基于Word2vec的句子语义相似度计算研究[J].计算机科学,2017, 44(9): 256-260.(LI X, JIE H,LI L J. Research on sentence semantic similarity calculation based on word2vec[J]. Computer science, 2017, 44(9): 256-260.) Cited in this article [1]

[24]	唐晓波,祝黎,谢力.基于主题的微博二级好友推荐模型研究[J].图书情报工作,2014, 58(9):105-113.(TANG X B, ZHU L, XIE L. Two-level microblog friend recommendation based on topic model[J]. Library and information service,2014, 58(9):105-113.) Cited in this article [1]

[25]	你好星期一.Word2vec参数[EB/OL]. [2022-12-13]. Cited in this article [1]

https://blog.csdn.net/DL_Iris/article/details/119175496 . (Hello on Monday. Word2vec parameter[EB/OL]. [2022-12-13].https://blog.csdn.net/DL_Iris/article/details/119175496.)

[26]	张谦,高章敏,刘嘉勇.基于Word2vec的微博短文本分类研究[J].信息网络安全, 2017(1):57-62. (ZHANG Q, GAO Z M, LIU J Y. Research of Weibo short text classfication based on word2ve[J]. Netinfo security, 2017(1):57-62.) Cited in this article [1]

[27]	Johnson A, Wright D. Identifying idiolect in forensic authorship attribution: an N-gram text bite approach[J].Language and law, 2014,1(1):37-69. Cited in this article [1]