Detecting Clickbait in Online Social Media: You Won’t Believe How We Did It

Elyashar, Aviad; Bendahan, Jorge; Puzis, Rami

doi:10.1007/978-3-031-07689-3_28

Cited by 6 publications

(2 citation statements)

References 16 publications

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“…Penggunaan model Support Vector Machine dan Logistic Regression menghasilkan akurasi pada kisaran 78% -79% [5]. Penggunaan model machine learning lainnya seperti XGBoost (eXtreme Gradient Boosting) Classifier berhasil memperoleh akurasi sebesar 81,2% dalam pengklasifikasian judul clickbait dari media sosial Twitter [6]. Sedangkan pada penerapan deep learning penggunaan model LSTM berhasil memperoleh akurasi 97% dengan mengambil data dari judul-judul berita di platform Reddit [7].…”

Section: A Pendahuluanunclassified

Perbandingan Kinerja LSTM, Bi-LSTM, dan GRU pada Klasifikasi Judul Berita Clickbait

Anas Fikri Hanif,

Theopilus Bayu Sasongko,

Arif Dwi Laksito

2023

ijcs

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Maraknya penggunaan konsep clickbait menjadi tantangan bagi para pengguna media sosial. Sering kali mereka tertipu dengan judul sebuah artikel yang berbeda dengan isi artikelnya. Oleh karena itu diperlukan sebuah model yang mampu melakukan klasifikasi terhadap judul clickbait maupun non-clickbait. Meskipun beberapa penelitian sudah dilakukan untuk membuat sebuah model klasifikasi judul clickbait, akan tetapi analisa perbandingan sangat diperlukan untuk menentukan model terbaik yang dapat digunakan dalam klasifikasi judul clickbait. Oleh karenanya peneliti melakukan perbandingan terhadap tiga model deep learning yang berbeda (LSTM, Bi-LSTM, dan GRU) guna menemukan model terbaik yang dapat menyelesaikan kasus ini dengan memanfaatkan data publik dari penelitian sebelumnya. Hasilnya algoritma GRU merupakan algoritma terbaik yang berhasil mencapai akurasi 97,16%. Tidak hanya itu GRU juga memiliki nilai tinggi dalam beberapa metrik evaluasi lainnya, yaitu precision 96,63%, recall 97,66%, dan F1-score 97,14%. Selain menghasilkan metrik evaluasi yang baik, model GRU juga tergolong cepat dalam melakukan training dengan waktu 328 detik.

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Section: A Pendahuluanunclassified

Perbandingan Kinerja LSTM, Bi-LSTM, dan GRU pada Klasifikasi Judul Berita Clickbait

Anas Fikri Hanif,

Theopilus Bayu Sasongko,

Arif Dwi Laksito

2023

ijcs

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“…Clickbait refers to messages designed to entice readers to click on a link by evoking their emotions and interests. This kind of writing is typically employed for website advertising, however studies have shown that clickbait is a key source of falsified information transmission on social media [10]. Troll refers to a person who wants to manoeuvre people to change their opinions in a discussion or to make them angry by acting in a violent manner.…”

Section: A Misinformation In Osnsmentioning

confidence: 99%

Rumor Stance Classification in Online Social Networks: The State-of-the-Art, Prospects, and Future Challenges

et al. 2022

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The emergence of the Internet as a ubiquitous technology has facilitated the rapid evolution of social media as the leading virtual platform for communication, content sharing, and information dissemination. In spite of revolutionizing the way news is delivered to people, this technology has also brought along with itself inevitable demerits. One such drawback is the spread of rumors expedited by social media platforms, which may provoke doubt and fear. Therefore, it is essential to debunk rumors before their widespread use. Over the years, many studies have been conducted to develop effective rumor verification systems. One aspect of such studies focuses on rumor stance classification, which involves the task of utilizing user viewpoints regarding a rumorous post to better predict the veracity of a rumor. Relying on user stances in rumor verification has gained significant importance, for it has resulted in significant improvements in the model performance. In this paper, we conduct a comprehensive literature review of rumor stance classification in complex online social networks (OSNs). In particular, we present a thorough description of these approaches and compare their performances. Moreover, we introduce multiple datasets available for this purpose and highlight their limitations. Finally, challenges and future directions are discussed to stimulate further relevant research efforts.

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