Abstract
Twitter has nowadays become a trending microblogging and social media platform for news and discussions. Since the dramatic increase in its platform has additionally set off a dramatic increase in spam utilization in this platform. For Supervised machine learning, one always finds a need to have a labeled dataset of Twitter. It is desirable to design a semi-supervised labeling technique for labeling newly prepared recent datasets. To prepare the labeled dataset lot of human affords are required. This issue has motivated us to propose an efficient approach for preparing labeled datasets so that time can be saved and human errors can be avoided. Our proposed approach relies on readily available features in real-time for better performance and wider applicability. This work aims at collecting the most recent tweets of a user using Twitter streaming and prepare a recent dataset of Twitter. Finally, a semi-supervised machine learning algorithm based on the self-training technique was designed for labeling the tweets. Semi-supervised support vector machine and semi-supervised decision tree classifiers were used as base classifiers in the self-training technique. Further, the authors have applied K means clustering algorithm to the tweets based on the tweet content. The principled novel approach is an ensemble of semi-supervised and unsupervised learning wherein it was found that semi-supervised algorithms are more accurate in prediction than unsupervised ones. To effectively assign the labels to the tweets, authors have implemented the concept of voting in this novel approach and the label pre-directed by the majority voting classifier is the actual label assigned to the tweet dataset. Maximum accuracy of 99.0% has been reported in this paper using a majority voting classifier for spam labeling.
Similar content being viewed by others
References
Abuliash M, Fazil M (2018) A hybrid approach for detecting automated spammers in twitter. IEEE Trans Inform Forensics Security 13(11):2707–2719
Al-Zoubi AM, Alqatawna J and Faris H (2017) Spam profile detection in social networks based on public features. 8th Int Conf Inform Comm Syst (ICICS), 130-135
Bazzaz Abkenar, S., Mahdipour, E., Jameii, S., & Haghi Kashani, M. (2021). A hybrid classification method for twitter spam detection based on differential evolution and random forest. Concurrency And Computation: Practice And Experience. https://doi.org/10.1002/cpe.6381.
Benevenuto F, Magno G, Rodrigues T and Almeida V (2010) Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), 6:12–22.
Chakraborty A, Sundi J and Satapathy S (2012) SPAM: a framework for social profile abuse monitoring. CSE508 report, Stony Brook University, stony brook, NY.
Eshraqi N, Jalali M and Moattar MH (2015) Detecting Spam tweets in twitter using a data stream clustering algorithm. International Congress on Technology, Communication and Knowledge (ICTCK), 347–351
Gautam G and Yadav D (2014) Sentiment analysis of twitter data using machine learning approaches and semantic analysis. Seventh Int Conf Contemp Comp (IC3), 1-6.
Herzallah W, Faris H, Adwan O (2018) Feature engineering for detecting spammers on twitter: modelling and analysis. J Inf Sci 44(2):230–247
Inuwa-Dutse I, Liptrott M, Korkontzelos I (2018) Detection of spam-posting accounts on twitter. Neurocomputing 315:496–511
Lin PC and Huang PM (2013) A study of effective features for detecting long-surviving twitter spam accounts. 15th Int Conf Adv Comm Technol (ICACT), 841-846.
Liu C and Wang G (2016) Analysis and detection of Spam accounts in social networks. 2nd IEEE Int Conf Comp Comm, 2526-2530
Peikari M, Salsms S, Nofech-Mozes S, Martel A (2018) A cluster-then-label semi-supervised learning approach for pathology image classification. Sci Rep 8(1):1–13
Sedhai S, Sun A (2018) Semi-supervised spam detection in twitter stream. IEEE Trans Comp Soc Syst 5(1):169–175
Stringhini G, Kruegel C and Vigna G (2010) Detecting spammers on social networks. Proceed 26th Ann Comp Sec Appl Conf (ACSAC), 1-9
Sun, N., Lin, G., Qiu, J., & Rimba, P. (2020). Near real-time twitter spam detection with machine learning techniques. Int J Comp Appl. 1-11. https://doi.org/10.1080/1206212x.2020.1751387
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Authors declared that they have no conflict of interest in this work.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Jan, T.G., Khurana, S.S. & Kumar, M. Semi-supervised labeling: a proposed methodology for labeling the twitter datasets. Multimed Tools Appl 81, 7669–7683 (2022). https://doi.org/10.1007/s11042-022-12221-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-12221-7