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All Journal Nusantara Journal of Computers and its Applications
Fitrah Maharani Humaira
Departemen Informatika dan Komputer, Politeknik Elektronika Negeri Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Other

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TWITTER BUZZER DETECTION SYSTEM USING TWEET SIMILARITY FEATURE AND SUPPORT VECTOR MACHINE Ahmad Mustofa; Fitrah Maharani Humaira; Myrna Ermawati; Peni Sriwahyu Natasari; Akhmad Arif Kurdianto; Aries Alfian Prasetyo; A Labib Fardany Faisal
NJCA (Nusantara Journal of Computers and Its Applications) Vol 8, No 1 (2023): June 2023
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v8i1.306

Abstract

Over the past few years, people have been able to get and share information through social media easily. Some of that information can be a false issue created by a buzzer account that intends to influence people into a specific opinion. Politicians often use social media to maintain a good image in society by utilizing buzzer accounts. The main characteristic of a buzzer account is that they upload the same content repeatedly within a certain period. Before analyzing data taken from social media such as Twitter, we need a buzzer detection system to filter data from buzzer users.  This research attempts to build a buzzer detection system using text processing and classification method. We use the similarity of tweets as a feature for the buzzer detection system by applying Cosine Similarity to the Term Frequency - Inverse Document Frequency (TF-IDF) feature of the tweets. In addition, we will use other features such as the number of followers, number of followings, the intensity of tweets, the ratio of retweets, and the ratio of tweets that contain links as additional features in this study. This research uses these features as inputs to the Support Vector Machine model to determine whether an account is a buzzer or not. This system has promising results by having 89% accuracy, 86.67% precision, 70.91 % recall, and 78% F1-score.
Co-Authors A Labib Fardany Faisal Ahmad Mustofa Akhmad Arif Kurdianto Aries Alfian Prasetyo Myrna Ermawati Peni Sriwahyu Natasari