Real-Time Twitter Data Analysis: A Survey
DOI:
https://doi.org/10.21928/uhdjst.v6n2y2022.pp147-155Keywords:
Twitter, Data Analysis, Twitter Streaming Application Programming Interface, Sentiment Analysis, Bot DetectionAbstract
Internet users are used to a steady stream of facts in the contemporary world. Numerous social media platforms, including Twitter, Facebook, and Quora, are plagued with spam accounts, posing a significant problem. These accounts are created to trick unwary real users into clicking on dangerous links or to continue publishing repetitious messages using automated software. This may significantly affect the user experiences on these websites. Effective methods for detecting certain types of spam have been intensively researched and developed. Effectively resolving this issue might be aided by doing sentiment analysis on these postings. Hence, this research provides a background study on Twitter data analysis, and surveys existing papers on Twitter sentiment analysis and fake account detection and classification. The investigation is restricted to the identification of social bots on the Twitter social media network. It examines the methodologies, classifiers, and detection accuracies of the several detection strategies now in use.
References
D. M. Kancherla. “A Hybrid Approach for Detecting Automated Spammers in Twitter”. International Educational Applied Research Journal, vol. 3, no. 9, 2707-2719, 2019.
J. Rodríguez-Ruiz, J. I. Mata-Sánchez, R. Monroy, O. Loyola- González and A. López-Cuevas. “A one-class classification approach for bot detection on Twitter”. Computers and Security, vol. 91, p. 101715, 2020.
O. Loyola-González, R. Monroy, J. Rodríguez, A. L. Cuevas and J. I. Sánchez. “Contrast pattern-based classification for bot detection on twitter”. IEEE Access, vol. 7, pp. 45800-45817, 2019.
N. A. Azeez, O. Atiku, S. Misra, A. Adewumi, R. Ahuja and R. Damaševičius. “Detection of malicious URLs on twitter”. Advances in Electrical and Computer Technologies, vol. 672, pp. 309-318, 2020.
J. Chen. “Twitter Metrics: How and Why You Should Track Them”. Sprout Social, United States. 2021. Available from: https:// sproutsocial.com/insights/twitter-metrics/. [Last accessed on Nov 2021 23].
S. Arifuzzaman and N. S. Sattar. “COVID-19 vaccination awareness and aftermath: Public sentiment analysis on twitter data and vaccinated population prediction in the USA”. Applied Sciences, vol. 11, no. 14, p. 6128, 2021.
O. Inya. “Egungun be careful, na Express you dey go: Socialising a newcomer-celebrity and co-constructing relational connection on Twitter Nigeria”. Journal of Pragmatics, vol. 184, pp. 140-151, 2021.
H. Piedrahita-Valdés, D. Piedrahita-Castillo, J. Bermejo-Higuera, P. Guillem-Saiz, J. R. Bermejo-Higuera, J. Guillem-Saiz, J. A. Sicilia-Montalvo and F. Machío-Regidor. “Vaccine hesitancy on social media: Sentiment analysis from June 2011 to April 2019”. Vaccines, vol. 9, no. 1, p. 28, 2019.
A. C. Breu. “From tweetstorm to tweetorials: Threaded tweets as a tool for medical education and knowledge dissemination”. Seminars in Nephrology, vol. 40, no. 3, pp. 273-278, 2020.
C. Wukich. “Connecting mayors: The content and formation of twitter information networks”. Urban Affairs Review, vol. 58, pp. 33- 67, 2020.
N. Aguilar-Gallegos, L. E. Romero-García, E. G. Martínez- González, E. I. García-Sánchez and J. Aguilar-Ávilaa. “Dataset on dynamics of coronavirus on twitter”. Data in Brief, vol. 30, p. 105684, 2020.
S. Boon-Itt and Y. Skunkan. “Public perception of the COVID-19 pandemic on twitter: Sentiment analysis and topic modeling study”. JMIR Public Health Surveill, vol. 6, no. 4, p. e21978, 2020.
V. Cheplygina, F. Hermans, C. Albers, N. Bielczyk and I. Smeets. “Ten simple rules for getting started on Twitter as a scientist”. PLoS Computational Biology, vol. 16, no. 2, p. e1007513, 2020.
R. Chandrasekaran, V. Mehta, T. Valkunde and E. Moustakas. “Topics, trends, and sentiments of tweets about the COVID-19 pandemic: Temporal infoveillance study”. Journal of Medical Internet Research, vol. 22, no. 10, p. e22624, 2020.
P. Surowiec and C. Miles. “The populist style and public diplomacy: Kayfabe as performative agonism in Trump’s Twitter posts”. Public Relations Inquiry, vol. 10, no. 1, pp. 5-30, 2021.
I. A. Mohammed and A. S. Abbas. “Twitter APIs for collecting data of influenza viruses, a systematic review”. 2021 International Conference on Communication and Information Technology (ICICT), vol. 12, pp. 256-261, 2021.
S. Wu, M. A. Rizoiu and L. Xie. “Variation across scales: Measurement fidelity under twitter data sampling”. Fourteenth International AAAI Conference on Web and Social Media, vol. 14, no. 1, pp. 715-725, 2020.
H. Ledford. “How Facebook, Twitter and other data troves are revolutionizing social science”. Nature, vol. 582, no. 7812, pp. 328- 330, 2020.
Z. Pehlivan, J. Thièvre and T. Drugeon. “Archiving social media: The case of Twitter”. The Past Web. Springer, Cham. pp. 43-56, 2021.
I. Nazeer, S. K. Gupta, M. Rashid and A. Kumar. “Use of novel ensemble machine learning approach for social media sentiment analysis”. Analyzing Global Social Media Consumption. Information Science Reference, Hershey. pp. 61-28, 2020.
R. Al Bashaireh, M. Zohdy and V. Sabeeh. “Twitter Data Collection and Extraction: A Method and A New Dataset, the UTD-MI”. ICISDM 2020: Proceedings of the 2020 the 4th International Conference on Information System and Data Mining, pp. 71-76, 2020.
R. P. Mehta, M. A. Sanghvi, D. K. Shah and A. Singh. “Sentiment Analysis of Tweets Using Supervised Learning Algorithms”. First International Conference on Sustainable Technologies for Computational Intelligence. vol. 1045, pp. 323-338, 2019.
A. S. Neogi, K. A. Garga, R. K. Mishraa, Y. K. Dwivedib. “Sentiment analysis and classification of Indian farmers’ protest using twitter data”. International Journal of Information Management Data Insights, vol. 1, no. 1, p. 100019, 2022.
T. Swathi, N. Kasiviswanath and A. A. Rao. “An optimal deep learning-based LSTM for stock price prediction using twitter sentiment analysis”. Applied Intelligence, vol. 52, pp. 13675- 13688, 2022.
A. D. Dubey. “Twitter sentiment analysis during COVID-19 outbreak”. Jaipuria Institute of Management, vol. 9, pp. 71-76, 2020.
C. Monica and N. Nagaraju. Detection of fake tweets using sentiment analysis”. SN Computer Science, vol. 1, no. 2, p. 89, 2020.
G. A. Ruz, P. A. Henríquez and A. Mascareño. “Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers”. Future Generation Computer Systems, vol. 106, pp. 92-104, 2020.
S. E. Saad and J. Yang. “Twitter sentiment analysis based on ordinal regression”. IEEE Access, vol. 7, pp. 163677-163685, 2019.
S. Kudugunta and E. Ferrara. “Deep neural networks for bot detection”. Information Sciences, vol. 467, pp. 312-322, 2018.
Z. Alom, B. Carminati, E. Ferrarib. “A deep learning model for Twitter spam detection”. Online Social Networks and Media, vol. 18, p. 100079, 2020.
F. Wei and U. T. Nguyen. “Twitter Bot Detection Using Bidirectional Long Short-Term Memory Neural Networks and Word Embeddings”. 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 101-109, 2019.
F. Martinelli, F. Mercaldo and A. Santone. “Social Network Polluting Contents Detection through Deep Learning Techniques”. 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1-10, 2019.
Q. Gong, Y. Chen, X. He, Z. Zhuang, T. Wang, H. Huang, X. Wang and X. Fu. “DeepScan: Exploiting deep learning for malicious account detection in location-based social networks”. IEEE Communications Magazine, vol. 56, no. 11, pp. 21-27, 2018.
Z. Gilani, E. Kochmar and J. Crowcroft. “Classification of twitter accounts into automated agents and human users”. Association for Computing Machinery, vol. 17, p. 489-496, 2017.
I. Pozzana, E. Ferrara. “Measuring bot and human behavioral dynamics”. Human Computer Interaction, vol. 1, 1-11, 2018.
A. H. Wang. “Detecting spam bots in online social networking sites: A machine learning approach”. In: S. Foresti and S. Jajodia (Eds.), Data and Applications Security and Privacy XXIV. vol. 6166, pp. 335-342, 2010.
Z. Chu, S. Gianvecchio, S. Jajodia H. Wang. Detecting automation of twitter accounts: Are you a human, bot, or cyborg?”. IEEE Transactions on Dependable and Secure Computing, vol. 9, pp. 811-824, 2012.
I. Pozzana and E. Ferrara. “Measuring bot and human behavioral dynamics”. Frontiers in Physics, vol. 8, no. 125, p. 32, 2020.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Hakar Mohammed Rasul, Alaa Khalil Jumaa
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
