Buzzer Detection to Maintain Information Neutrality in 2019 Indonesia Presidential Election

Panatra, Adrian Julianto; Chandra, Felisitus Bryan; Darmawan, Wiliam; Warnars, Harco Leslie Hendric Spits; Utomo, Wiranto Herry; Matsuo, Tokuro

doi:10.1109/iiai-aai.2019.00177

Cited by 2 publications

(3 citation statements)

References 8 publications

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“…Studies on fake news and hoax information on various social media platforms have become a research topic that has attracted many researchers to date, as was conducted by [3] on Facebook, [4] on Instagram, and also by [5] on Twitter. Studies using social media data are mostly done on Twitter social media, this is due to the ease of crawling research data on Twitter in general, it is easier for the public to do than crawling other social media data.…”

Section: Introductionmentioning

confidence: 99%

“…6 No. 4 (2022) DOI: https://doi.org/10.29207/resti.v6i4.4338 Creative Commons Attribution 4.0 International License (CC BY 4.0) 664 [4], [9], [10]. In a study conducted by Bradshaw [11] buzzer in Indonesia itself has been categorized as a low cyber troop army.…”

Section: Introductionmentioning

confidence: 99%

“…Some important findings obtained from some of these researches include bot accounts having a lower ratio of the number of followers or followings compared to real accounts, posting times made by bot accounts are more regular, and bot accounts, in general, have a younger account age than the original account. In detecting buzzer accounts, the original user metadata and activity features are generally used, both on Instagram [4] and Twitter [5], [16]. But considering the phenomenon of the emergence of buzzer accounts not only in these periods, but research on the detection of the buzzer account in different periods is also interesting to do.…”

Section: Introductionmentioning

confidence: 99%

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Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Sibaroni

Prasetiyowati

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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The rapid use of Twitter social media in recent times has an impact on the faster dissemination of disinformation which is very dangerous to followers. Detection of disinformation is very important to do and can be done manually by conducting in-depth information analysis. But given the huge amount of information, this approach is less effective. Another, more effective approach is to use a machine learning-based approach. Several studies on hoax information detection based on machine learning have been carried out where some studies analyze the content of a tweet and some others analyze hashtags which are the context of a tweet. The feature usually used to analyze hashtag sentiment data is the property feature of the creator's account. The creator accounts of disinformation are called buzzer accounts. This research proposes account property feature expansion of buzzer accounts combined with the SVM classifier which in several previous similar studies has a very good performance to detect the buzzer hashtag. The experimental results show that expanding the proposed feature can increase SVM's performance in detecting hashtag buzzers by more than 24% compared to using the baseline feature, and the average F1 score obtained from the combination of methods is 84%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Sibaroni

Prasetiyowati

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Feature Selection and Performance Evaluation of Buzzer Classification Model

Afra,

Fajri,

Prafitia

et al. 2024

J. Optimasi Sist. Ind.

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In the rapidly evolving digital age, social media platforms have transformed into battleground for shaping public opinion. Among these platforms, X has been particularly susceptible to the phenomenon of 'buzzers', paid or coordinated actors who manipulate online discussions and influence public sentiment. This manipulation poses significant challenges for users, researchers, and policymakers alike, necessitating robust detection measures and strategic feature selection for accurate classification models. This research explores the utilization of various feature selection techniques to identify the most influential features among the 24 features employed in the classification modeling using Support Vector Machine. This study found that selecting 11 key features yields a remarkably effective classification model, achieving an impressive F1-score of 87.54 in distinguishing between buzzer and non-buzzer accounts. These results suggest that focusing on the relevant features can improve the accuracy and efficiency of buzzer detection models. By providing a more robust and adaptable solution to buzzer detection, our research has the potential to advance social media research and policy. This enabling researchers and policymakers to devise strategies aimed at mitigating misinformation dissemination and cultivating an environment of trust and integrity within social media platforms, thus fostering healthier online interactions and discourse.

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Buzzer Detection to Maintain Information Neutrality in 2019 Indonesia Presidential Election

Cited by 2 publications

References 8 publications

Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Buzzer Detection on Indonesian Twitter using SVM and Account Property Feature Extension

Feature Selection and Performance Evaluation of Buzzer Classification Model

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