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Annals of Data Science

20.07.2024

A Human Word Association Based Model for Topic Detection in Social Networks

verfasst von: Mehrdad Ranjbar-Khadivi, Shahin Akbarpour, Mohammad-Reza Feizi-Derakhshi, Babak Anari

Erschienen in: Annals of Data Science

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Abstract

With the widespread use of social networks, detecting the topics discussed on these platforms has become a significant challenge. Current approaches primarily rely on frequent pattern mining or semantic relations, often neglecting the structure of the language. Language structural methods aim to discover the relationships between words and how humans understand them. Therefore, this paper introduces a topic detection framework for social networks based on the concept of imitating the mental ability of word association. This framework employs the Human Word Association method and includes a specially designed extraction algorithm. The performance of this method is evaluated using the FA-CUP dataset, a benchmark in the field of topic detection. The results indicate that the proposed method significantly improves topic detection compared to other methods, as evidenced by Topic-recall and the keyword F1 measure. Additionally, to assess the applicability and generalizability of the proposed method, a dataset of Telegram posts in the Persian language is used. The results demonstrate that this method outperforms other topic detection methods.

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Has a higher occurrence frequency than others.

https://fasttext.cc/.

http://www.socialsensor.eu/results/datasets/72-twitter-tdt-dataset.

cominsys.ir.

Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4:149–178. https://doi.org/10.1007/s40745-017-0112-5CrossRef

Zehtab-Salmasi A, Feizi-Derakhshi A-R, Nikzad-Khasmakhi N, Asgari-Chenaghlu M, Nabipour S (2023) Multimodal price prediction. Ann Data Sci 10:619–635. https://doi.org/10.1007/s40745-021-00326-zCrossRef

Reihanian A, Feizi-Derakhshi M-R, Aghdasi HS (2018) Overlapping community detection in rating-based social networks through analyzing topics, ratings and links. Pattern Recognit 81:370–387. https://doi.org/10.1016/j.patcog.2018.04.013CrossRef

Asgari-Chenaghlu M, Nikzad-Khasmakhi N, Minaee S (2020) Covid-transformer: detecting COVID-19 trending topics on Twitter using universal sentence encoder. https://doi.org/10.48550/arXiv.2009.03947

Shi Y, Tian Y, Kou G, Peng Y, Li J (2011) Optimization based data mining: theory and applications. Springer, London. https://doi.org/10.1007/978-0-85729-504-0CrossRef

Aiello LM, Petkos G, Martin C, Corney D, Papadopoulos S, Skraba R, Goker A, Kompatsiaris I, Jaimes A (2013) Sensing trending topics in twitter. IEEE Trans Multimed. https://doi.org/10.1109/TMM.2013.2265080CrossRef

Uhr P, Klahold A, Fathi M (2013) Imitation of the human ability of word association. Int J Soft Comput Softw Eng [JSCSE] 3:248–254. https://doi.org/10.7321/jscse.v3.n3.37.3CrossRef

Klahold A, Uhr P, Ansari F, Fathi M (2014) Using word association to detect multitopic structures in text documents. IEEE Intell Syst 29:40–46. https://doi.org/10.1109/MIS.2013.120.2CrossRef

Saeed Z, Abbasi RA, Razzak I, Maqbool O, Sadaf A, Xu G (2019) Enhanced heartbeat graph for emerging event detection on twitter using time series networks. Expert Syst Appl 136:115–132. https://doi.org/10.1016/j.eswa.2019.06.005CrossRef

10.

Indra Winarko E, Pulungan R (2018) Trending topics detection of Indonesian tweets using bn-grams and doc-p. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2018.01.005CrossRef

11.

Choi H-J, Park CH (2019) Emerging topic detection in twitter stream based on high utility pattern mining. Expert Syst Appl 115:27–36. https://doi.org/10.1016/j.eswa.2018.07.051CrossRef

12.

Gaglio S, Lo Re G, Morana M (2015) Real-time detection of twitter social events from the user’s perspective. In: 2015 IEEE international conference on communications (ICC), pp 1207–1212 . https://doi.org/10.1109/ICC.2015.7248487

13.

Asgari-Chenaghlu M, Feizi-Derakhshi M-R, Farzinvash L, Balafar M-A, Motamed C (2021) Topic detection and tracking techniques on Twitter: a systematic review. Complexity 2021:1–15. https://doi.org/10.1155/2021/8833084CrossRef

14.

Liu M, Qu J (2012) Mining high utility itemsets without candidate generation. In: Proceedings of the 21st ACM international conference on information and knowledge management—CIKM ’12, p 55. ACM Press, New York, New York, USA . https://doi.org/10.1145/2396761.2396773

15.

Saeed Z, Abbasi RA, Razzak MI, Xu G (2019) Event detection in twitter stream using weighted dynamic heartbeat graph approach [application notes]. IEEE Comput Intell Mag 14:29–38. https://doi.org/10.1109/MCI.2019.2919395CrossRef

16.

Cordeiro M, Gama J (2016) In: Michaelis S, Piatkowski N, Stolpe M (eds) Online social networks event detection: a survey, pp 1–41. Springer, Cham. https://doi.org/10.1007/978-3-319-41706-6_1

17.

Zong C, Xia R, Zhang J (2021) Topic detection and tracking. Springer, Singapore, pp 201–225. https://doi.org/10.1007/978-981-16-0100-2_9CrossRef

18.

Petrović S, Osborne M, Lavrenko V (2010) Streaming first story detection with application to Twitter. In: Kaplan R, Burstein J, Harper M, Penn G (eds) Human language technologies: the 2010 annual conference of the North American chapter of the association for computational linguistics, pp 181–189. Association for Computational Linguistics, Los Angeles, California. https://aclanthology.org/N10-1021

19.

Allan J, Lavrenko V, Malin D, Swan R (2000) Detections, bounds, and timelines: Umass and tdt-3. In: Proceedings of topic detection and tracking workshop, pp 167–174

20.

Huang J, Peng M, Wang H (2015) Topic detection from large scale of microblog stream with high utility pattern clustering. In: Proceedings of the 8th workshop on Ph.D. workshop in information and knowledge management, pp 3–10. https://doi.org/10.1145/2809890.2809894

21.

Saeed Z, Ayaz Abbasi R, Razzak I (2020) Evesense: What can you sense from twitter? In: Jose JM, Yilmaz E, Magalhães J, Castells P, Ferro N, Silva MJ, Martins F (eds) Advances in information retrieval. Springer, Cham, pp 491–495. https://doi.org/10.1007/978-3-030-45442-5_64CrossRef

22.

Blei D, Ng A, Jordan M (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022

23.

Oâ€™Connor B, Krieger M, Ahn D (2010) Tweetmotif: exploratory search and topic summarization for twitter. In: Proceedings of the international AAAI conference on web and social media 4(1):384–385 . https://doi.org/10.1609/icwsm.v4i1.14008

24.

Petkos G, Papadopoulos S, Aiello L, Skraba R, Kompatsiaris Y (2014) A soft frequent pattern mining approach for textual topic detection. In: Proceedings of the 4th international conference on web intelligence, mining and semantics (WIMS14)—WIMS ’14, pp 1–10. ACM Press, New York, New York, USA. https://doi.org/10.1145/2611040.2611068

25.

Elbagoury A, Ibrahim R, Farahat A, Kamel M, Karray F (2021) Exemplar-based topic detection in twitter streams. Proceedings of the International AAAI Conference on Web and Social Media 9(1):610–613. https://doi.org/10.1609/icwsm.v9i1.14651

26.

Prabandari R.D, Murfi H (2017) Comparative study of original recover and recover KL in separable non-negative matrix factorization for topic detection in Twitter. In: AIP conference proceedings. 1862(1):030144. https://pubs.aip.org/aip/acp/article-pdf/doi/10.1063/1.4991248/13606806/030144_1_online.pdf. https://doi.org/10.1063/1.4991248

27.

Nur’aini K, Najahaty I, Hidayati L, Murfi H, Nurrohmah S (2015) Combination of singular value decomposition and k-means clustering methods for topic detection on twitter. In: 2015 International conference on advanced computer science and information systems (ICACSIS), pp 123–128. https://doi.org/10.1109/ICACSIS.2015.7415168

28.

Li C, Sun A, Datta A (2012) Twevent: segment-based event detection from tweets. In: Proceedings of the 21st ACM international conference on information and knowledge management. In: CIKM ’12, pp 155–164. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/2396761.2396785

29.

Saeed Z, Abbasi R.A, Sadaf A, Razzak MI, Xu G (2018) Text stream to temporal network—a dynamic heartbeat graph to detect emerging events on Twitter, pp 534–545. https://doi.org/10.1007/978-3-319-93037-4_42

30.

Asgari-Chenaghlu M, Feizi-Derakhshi M-R, Farzinvash L, Balafar M-A, Motamed C (2021) TopicBERT: a cognitive approach for topic detection from multimodal post stream using BERT and memoryâ€“graph. Chaos Solitons Fractals 151:111274. https://doi.org/10.1016/j.chaos.2021.111274CrossRef

31.

Hadizadeh Moghaddam A, Momtazi S (2024) A semantic modular framework for events topic modeling in social media. Multimed Tools Appl 83:10755–10778. https://doi.org/10.1007/s11042-023-15745-8CrossRef

32.

Steyvers M, Shiffrin RM, Nelson DL (2004) Word association spaces for predicting semantic similarity effects in episodic memory. In: Healy AF (ed) Experimental cognitive psychology and its applications. American Psychological Association, pp 237–249. https://doi.org/10.1037/10895-018CrossRef

33.

Nelson DL, McEvoy CL, Dennis S (2000) What is free association and what does it measure? Memory Cognit. https://doi.org/10.3758/BF03209337CrossRef

34.

Nelson DL, McEvoy CL, Schreiber TA (2004) The University of South Florida free association, rhyme, and word fragment norms. Behav Res Methods Instrum Comput. https://doi.org/10.3758/BF03195588CrossRef

35.

Russell WA (1970) The complete German language norms for responses to 100 words from the Kent–Rosanoff word association test, pp 53–94. https://doi.org/10.1016/B978-0-12-563050-4.50006-6

36.

VasfiSisi N, Feizi Derakhshi MR (2013) Text classification with machine learning algorithms. J Basic Appl Sci Res 3(1):31–35

37.

Shi Y (2022) Advances in big data analytics: theory. Algorithms and Practices. https://doi.org/10.1007/978-981-16-3607-3

38.

Bojanowski P, Grave E, Joulin A, Mikolov T (2016) Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606

39.

Campello RJGB, Moulavi D, Sander J (2013) Density-based clustering based on hierarchical density estimates. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 7819 LNAI, pp 160–172. https://doi.org/10.1007/978-3-642-37456-2_14. http://link.springer.com/10.1007/978-3-642-37456-2_14

40.

Olson D, Shi Y (2007) Introduction to business data mining

41.

Ranjbar-Khadivi M, Akbarpour S, Feizi-Derakhshi M-R, Anari B (2023) Persian topic detection based on human word association and graph embedding. https://doi.org/10.48550/arXiv.2302.09775

42.

Ranjbar-Khadivi M, Feizi-Derakhshi M-R, Forouzandeh A, Gholami P, Feizi-Derakhshi A-R, Zafarani-Moattar E (2022) Sep_TD_Tel01 . https://doi.org/10.17632/372rnwf9pc

43.

Benny A, Philip M (2015) Keyword based tweet extraction and detection of related topics. Procedia Comput Sci 46:364–371. https://doi.org/10.1016/j.procs.2015.02.032.1CrossRef

Titel: A Human Word Association Based Model for Topic Detection in Social Networks
verfasst von: Mehrdad Ranjbar-Khadivi
Shahin Akbarpour
Mohammad-Reza Feizi-Derakhshi
Babak Anari
Publikationsdatum: 20.07.2024
Verlag: Springer Berlin Heidelberg
Erschienen in: Annals of Data Science
Print ISSN: 2198-5804
Elektronische ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-024-00561-0

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