References
- N. Ibrahim, X. Wang, and H. Bourne, “Exploring the effect of user engagement in online brand communities: Evidence from twitter,” Comput. Human. Behav., Vol. 72, no. 1, pp. 321–38, 2017.
- M. Sreenivasulu, and M. Sridevi, “A survey on event detection methods on various social media,” Adv. Intell. Syst. Comput., Vol. 3, pp. 87–93, 2018.
- G. Ruz, P. Henríquez, and A. Mascareño, “Sentiment analysis of twitter data during critical events through Bayesian networks classifiers,” Future Gener. Comput. Syst., Vol. 106, no. 1, pp. 92–104, 2020.
- P. Rao, C. Kamhoua, L. Njilla, and K. Kwiat, “Methods to detect cyberthreats on twitter,” Surveillance Action, pp. 333–50, 2017.
- N. Ibrahim, and X. Wang, “A text analytics approach for online retailing service improvement: evidence from twitter,” Decis. Support. Syst., Vol. 121, no. 1, pp. 37–50, 2019.
- Y. Sumikawa, and A. Jatowt, “Analyzing history-related posts in twitter,” Int. J. Digit. Libr., Vol. 22, no. 1, pp. 105–34, 2020.
- Y. Çetinkaya, İ Toroslu, and H. Davulcu, “Developing a twitter bot that can join a discussion using state-of-the-art architectures,” Soc. Netw. Anal. Min., Vol. 10, no. 1, pp. 1–21, 2020.
- J. Beedasy, A. SamurZúñiga, T. Chandler, and T. Slack, “Online community discourse during the deepwater horizon oil spill: An analysis of twitter interactions,” Int. J. Disaster. Risk. Reduct., Vol. 51, pp. 101870, 2020.
- R. Nagamanjula, and A. Pethalakshmi, “A novel framework based on bi-objective optimization and LAN2FIS for twitter sentiment analysis,” Soc. Netw. Anal. Min., Vol. 10, pp. 34, 2020.
- B. Muindi, “Negotiating the balance between speed and credibility in deploying twitter as journalistic tool at the daily nation newspaper in Kenya,” Afr. Journalism Stud., Vol. 39, no. 1, pp. 111–28, 2018.
- F. H. Shajin, P. Rajesh, and M. R. Raja, “An efficient VLSI architecture for fast motion estimation exploiting zero motion prejudgment technique and a new quadrant-based search algorithm in HEVC,” Circuits Syst. Signal Process., pp. 1–24, 2021.
- P. Rajesh, S. Muthubalaji, S. Srinivasan, and F. H. Shajin, “Leveraging a dynamic differential annealed optimization and recalling enhanced recurrent neural network for maximum power point tracking in wind energy conversion system,” Technol. Econ. Smart Grids Sustainable Energy, Vol. 7, no. 1, pp. 1–5, 2022.
- F. H. Shajin, and P. Rajesh, “FPGA realization of a reversible data hiding scheme for 5G MIMO-OFDM system by chaotic key generation-based Paillier cryptography along with LDPC and its side channel estimation using machine learning technique,” J. Circuits Syst. Comput., Vol. 31, no. 05, pp. 2250093, 2022.
- P. Rajesh, F. H. Shajin, B. Mouli Chandra, and B. N. Kommula, “Diminishing energy consumption cost and optimal energy management of photovoltaic aided electric vehicle (PV-EV) by GFO-VITG approach,” Energy Sources Part A, pp. 1–9, 2021.
- M. Daniel, R. Neves, and N. Horta, “Company event popularity for financial markets using twitter and sentiment analysis,” Expert. Syst. Appl., Vol. 71, pp. 111–24, 2017.
- J. Choi, J. Yoon, J. Chung, B. Coh, and J. Lee, “Social media analytics and business intelligence research: A systematic review,” Inf. Process. Manag., Vol. 57, no. 6, pp. 102279, 2020.
- X. Liu, A. Burns, and Y. Hou, “An investigation of brand-related user-generated content on twitter,” J. Adversarial, Vol. 46, no. 2, pp. 236–47, 2017.
- R. Alharthi, A. Alhothali, and K. Moria, “Detecting and characterizing Arab spammers campaigns in twitter,” Procedia. Comput. Sci., Vol. 163, pp. 248–56, 2019.
- M. Celliers, and M. Hattingh, “A systematic review on fake news themes reported in literature,” Lect. Notes Comput. Sci., pp. 223–34, 2020.
- R. Zhao, and K. Mao, “Fuzzy bag-of-words model for document representation,” IEEE Trans. Fuzzy Syst., Vol. 26, no. 2, pp. 794–804, 2018.
- A. Marinoni, and P. Gamba, “Unsupervised data driven feature extraction by means of mutual information maximization,” IEEE Trans. Comput. Imaging, Vol. 3, no. 2, pp. 243–53, 2017.
- H. Alshaer, M. Otair, L. Abualigah, M. Alshinwan, and A. Khasawneh, “Feature selection method using improved CHI square on Arabic text classifiers: analysis and application,” Multimed. Tools. Appl., Vol. 80, no. 7, pp. 10373–90, 2020.
- H. Zhang, R. Wang, R. Pan, and H. Pan, “Imbalanced fault diagnosis of rolling bearing using enhanced generative adversarial networks,” IEEE. Access., Vol. 8, pp. 185950–63, 2020.
- A. Kaveh, and A. DadrasEslamlou, “Water strider algorithm: A new metaheuristic and applications,” Structures, Vol. 25, pp. 520–41, 2020.
- C. Monica, and N. Nagarathna, “Detection of fake Tweets using sentiment analysis,” SN Compu. Sci., Vol. 1, pp. 1–7, 2020.
- V. V. Hirlekar, and A. Kumar, “An empirical analysis of fake tweet detection using statistical and deep learning approaches,” in Information and communication technology for competitive strategies (ICTCS 2020), A. Joshi, M. Mahmud, R. G. Ragel, and N. V. Thakur, Eds. Singapore: Springer, 2022, pp. 1033–44.
- D. Kar, M. Bhardwaj, S. Samanta, and A. P. Azad, “No rumours please! a multi-indic-lingual approach for COVID fake-tweet detection,” in 2021 grace hopper celebration India (GHCI), Bangalore, India: IEEE, 2020, pp. 1–5.
- Y. Madani, M. Erritali, and B. Bouikhalene, “Using artificial intelligence techniques for detecting Covid-19 epidemic fake news in Moroccan tweets,” Results Phys., Vol. 25, pp. 104266, 2021.
- S. D. Das, A. Basak, and S. Dutta, “A heuristic-driven uncertainty based ensemble framework for fake news detection in tweets and news articles,” Neurocomputing, Vol. 491, pp. 607–620, 2021.
- J. A. Nasir, O. S. Khan, and I. Varlamis, “Fake news detection: A hybrid CNN-RNN based deep learning approach,” Int. J. Inf. Manage. Data Insights, Vol. 1, no. 1, pp. 100007, 2021.
- D. Abdelminaam, F. Ismail, M. Taha, A. Taha, E. Houssein, and A. Nabil, “CoAID-DEEP: An optimized intelligent framework for automated detecting COVID-19 misleading information on twitter,” IEEE. Access., Vol. 9, pp. 27840–67, 2021.
- https://github.com/tweepy/tweepy