Pre-processing of social media remarks for forensics

Conference or workshop item

Gao, Xuhao and Qi, Man 2023. Pre-processing of social media remarks for forensics. IEEE. https://doi.org/10.1109/icnc-fskd59587.2023.10280980
AuthorsGao, Xuhao and Qi, Man
Description

The Internet's rapid growth has led to a surge in social network users, resulting in an increase in extreme emotional and hate speech online. This study focuses on the security of public opinion in cyber security by analyzing Twitter data. The goal is to develop a model that can detect both sentiment and hate speech in user texts, aiding in the identification of content that may violate laws and regulations. The study involves pre processing the acquired forensic data, including tasks like lowercasing, stop word removal, and stemming, to obtain clear and effective data. This paper contributes to the field of public opinion security by linking forensic data with machine learning techniques, showcasing the potential for detecting and analyzing Twitter text data.

KeywordsPre-processing; Social media remarks; Forensic data; Cyber security; Data processing
Year2023
Conference19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2023)
Digital Object Identifier (DOI)https://doi.org/10.1109/icnc-fskd59587.2023.10280980
Official URLhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10280980&tag=1
Related URLhttp://81.68.97.198:8080/ICNC2023/index.htm
References

[1] Chow, Jacky. (2017). Analysis of Financial Credit Risk Using Machine
Learning. 10.13140/RG.2.2.30242.53449.
[2] Chen, Xingming & Rao, Yanghui & Xie, Haoran & Wang, Fu Lee &
Zhao, Yingchao & Yin, Jian. (2019). Sentiment Classification Using
Negative and Intensive Sentiment Supplement Information. Data
Science and Engineering. 10.1007/s41019-019-0094-8.
[3] Casey E. (2011). Digital evidence and computer crime: forensic science,
computers and the internet. Academy Press Publications, London. ISBN
978-0-12-374268
[4] Gover, A. R., Harper, S. B., & Langton, L. (2020). Anti-asian hate crime
during the covid-19 pandemic: Exploring the reproduction of inequality.
American Journal of Criminal Justice. Advance online publication.
https://doi.org/10.1007/s12103-020-09545-1
[5] Garfinkel SL. (2010). Digital forensic research: the next 10 years. In:
Proceedings of the 10th annual conference on digital forensic research
workshop. Digit Investig 7:S64–S73
[6] Harshith. 2019. Text Preprocessing in Natural Language Processing.
[online] Available at: https://towardsdatascience.com/textpreprocessing-in-natural-language-processing-using-python
[7] Hernandez-Suarez, A. & Sanchez-Perez, Gabriel & MartinezHernandez, V. & Perez-Meana, Hector & Toscano,
[8] Karina & Nakano-Miyatake, M. & Sanchez, Victor. (2017). Predicting
political mood tendencies based on Twitter data. 1-6.
10.1109/IWBF.2017.7935106.
[9] Hosmer C. (2006). Digital evidence bag. Commun ACM 49(2):69–70.
[10] Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014.
GloVe: Global Vectors for Word Representation. In Proceedings of the
2014 Conference on Empirical Methods in Natural Language Processing
(EMNLP), pages 1532–1543, Doha, Qatar. Association for
Computational Linguistics.
[11] Kumar, S., Zymbler, M. 2019. A machine learning approach to analyze
customer satisfaction from airline tweets. J Big Data 6, 62.
[12] Kathirgamanathan, A., Patel, A., Khwaja, A.S., Venkatesh, B.,
Anpalagan, A. (2022). Performance comparison of single and ensemble
CNN, LSTM and traditional ANN models for short-term electricity load
forecasting, J. Eng. 2022, 550– 565. https://doi.org/10.1049/tje2.12132
[13] Kamal, R., Hemdan, E.ED. & El-Fishway. (2021). N. A review study on
blockchain-based IoT security and forensics. Multimed Tools Appl 80,
36183–36214. https://doi.org/10.1007/s11042-021-11350-9
[14] Karina Reyes. (2020). Sentiment Analysis on Tweets with LSTM.
[online] Available at: https://medium.com/@karyrs1506/sentimentanalysis-on-tweets-with-lstm-22e3bbf93a61. Konikoff, D. (2021).
Gatekeepers of toxicity: Reconceptualizing Twitter's abuse and hate
speech policies. Policy Internet, 13, 502– 521.
https://doi.org/10.1002/poi3.265.
[15] Mikolov, T., Chen, K., Corrado, G.S., & Dean, J. (2013). Efficient
Estimation of Word Representations in Vector Space. ICLR.
[16] Mohammad Ehsan Basiri, Shahla Nemati, Moloud Abdar, Erik Cambria,
U. Rajendra Acharya. (2021). ABCDM: An Attention-based
Bidirectional CNN-RNN Deep Model for sentiment analysis. Future
Generation Computer Systems, Volume 115, 2021, Pages 279-294,
ISSN 0167-739X. https://doi.org/10.1016/j.future.2020.08.005.
[17] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever,
Ruslan Salakhutdinov. 2012. Dropout: A Simple Way to Prevent Neural
Networks from Overfitting. Journal of Machine Learning Research.
15(56):1929-1958, 2014.
[18] Pauly, Leo & Peel, Harriet & Luo, Shan & Hogg, David & Fuentes,
Raul. (2017). Deeper Networks for Pavement Crack Detection.
10.22260/ISARC2017/0066

Additional information

Publications router.

Journal2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
PublisherIEEE
Publication dates
Print29 Jul 2023
Publication process dates
Deposited30 Aug 2023
Output statusPublished
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