Detection of the Key Actor of Issues Spreading Based on Social Network Analysis in Twitter Social Media

Oroh, Audy Joize; Bandung, Yoanes; Zagi, Luqman Muhammad

doi:10.1109/apwimob51111.2021.9435268

Cited by 6 publications

(4 citation statements)

References 6 publications

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“…Closeness centrality measures the closeness of a node to others in the network and is obtained by calculating the average length (reciprocal) of the shortest paths between nodes (Oroh et al, 2021). According to Table 4, the bot account IDs with the highest closeness centrality are @NickRicci5, @GrumpyPing, @Ewacg, @Wandere61087892, and @Damian09733512.…”

Section: Social Network Analysis: Core Social Bots Within Swarmsmentioning

confidence: 99%

Influence of social bots in information warfare: A case study on @UAWeapons Twitter account in the context of Russia–Ukraine conflict

Liu

et al. 2023

Communication and the Public

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During the Russia–Ukraine conflict, social media has become an outlet for public opinion; therefore, besides the hot war, information warfare is also taking place. It was discovered that a large number of new social bot accounts had emerged on Twitter since the outbreak of the conflict. In particular, the Twitter account @UAWeapons has grown its following by hundreds of thousands in fewer than 30 days and has established itself as an influential opinion leader. Through data mining and three different analysis methods, this study investigated how social bots grew to influence public opinion. Specifically, time-series analysis revealed an unusual pattern of “pulsing” changes in the number of tweets posted by @UAWeapons over time. The content analysis illustrated that the account posted biased tweets in favor of Ukraine under the guise of being a neutral messenger, which led to frequent retweets from social bots with similar opinions. Finally, the results of the social network analysis indicated that @UAWeapons’ rapid growth might be attributed to a strategy of network clustering implemented by a core group of social bot accounts.

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Section: Social Network Analysis: Core Social Bots Within Swarmsmentioning

confidence: 99%

Influence of social bots in information warfare: A case study on @UAWeapons Twitter account in the context of Russia–Ukraine conflict

Liu

et al. 2023

Communication and the Public

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“…Beberapa penelitian terdahulu justru hanya fokus terhadap efek video storytelling atau analisis jejaring sosial saja, sehingga belum ada penelitian yang melihat video promosi storytelling yang berkaitan dengan jejaring sosial yang dipengaruhi dengan brand experience. Selain itu, penelitian terdahulu justru menganalisis data yang berada di Twitter (Barisione et al, 2019;Bassano et al, 2019;Berto & Muwarni, 2022;Calvi & Hover, 2021;Eriyanto, 2020;Fisanick & Stakeley, 2020;Kwon, 2019;Li & Liu, 2020;Lundqvist et al, 2013;Moin et al, 2020;Muwarni et al, 2021;Oroh et al, 2021;Rohimi, 2021;Surya Negara et al, 2021;Wahyu et al, 2022;Yao et al, 2021;Youssef et al, 2019;Yuliarti et al, 2021). Maka dari itu, penelitian ini berusaha menjawab efek video promosi pariwisata yang menggunakan teknik storytelling yang ditayangkan di YouTube memiliki pengaruh terhadap citra destinasi positif.…”

Section: Latar Belakangunclassified

Analisis Sentimen Jaringan Pesan Kolom Komentar Video Wonderful Indonesia 2022 Jagad Jawi Yang Dipengaruhi Budaya

Wahyu¹,

Berto²,

Murwani³

2022

12 Juni 2013

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Video Wonderful Indonesia 2022 Jiwa Jagad Jawi is a promotional video for Java Island tourist destinations that uses digital storytelling techniques. The researcher chose the promotional video because it was one of the storytelling videos that received high enthusiasm from the audience. The promotional video displays religious elements such as prayer traditions. Religious activities can be a sensitive topic. In this case, YouTube user comments can be interpreted as a special reaction given by the viewer. Comments can also be used as input for the Ministry of Tourism and Creative Economy in making tourism promotion videos. This study analyzed the social networks formed in tourism promotion videos. The method used in this study uses social media network analysis to analyze the level of network structure, groups, and actors. This research uses a mixed-method approach, which combines qualitative and quantitative research. The findings of this study are network structure in this promotional video is formed due to the topic of religion, not the topic of tourist destinations. This happened because the promotional video featured the tradition of prayer. Most of the comments made by the actors have positive connotations, expressing admiration for the island of Java. The results of this study indicate that the storytelling technique influences the image of a positive destination in the audience. However, the network structure that was formed was precise because of the topic of religion, not Java as a tourist destination, even though the word 'Java' has been mentioned 712 times.

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“…Rahim et al [6] collaborated centrality and similarity measurements used for friendly recommendation. Detection of the central or influential actors in a network is mainly out on social media, such as Twitter [7]- [12]. Besides, influential actors implement other fields, such as, cybercrime [13]- [15] and e-commerce [16].…”

Section: Introductionmentioning

confidence: 99%

An extended approach of weight collective influence graph for detection influence actor

Martono

Azhari

Mustofa

2022

Int. J. Adv. Intell. Informatics

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Over the last decade, numerous methods have been developed to detect the influential actors of hate speech in social networks, one of which is the Collective Influence (CI) method. However, this method is associated with unweighted datasets, which makes it inappropriate for social media, significantly using weight datasets. This study proposes a new CI method called the Weighted Collective Influence Graph (WCIG), which uses the weights and neighbor values to detect the influence of hate speech. A total of 49, 992 Indonesian tweets were and extracted from Indonesian Twitter accounts, from January 01 to January 22, 2021. The data collected are also used to compare the results of the proposed WCIG method to determine the influential actors in the dissemination of information. The experiment was carried out two times using parameters ∂=2 and ∂=4. The results showed that the usernames bernacleboy and zack_rockstar are influential actors in the dataset. Furthermore, the time needed to process WCIG calculations on HPC is 34-75 hours because the larger the parameter used, the greater the processing time.

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Detection of the Key Actor of Issues Spreading Based on Social Network Analysis in Twitter Social Media

Cited by 6 publications

References 6 publications

Influence of social bots in information warfare: A case study on @UAWeapons Twitter account in the context of Russia–Ukraine conflict

Influence of social bots in information warfare: A case study on @UAWeapons Twitter account in the context of Russia–Ukraine conflict

Analisis Sentimen Jaringan Pesan Kolom Komentar Video Wonderful Indonesia 2022 Jagad Jawi Yang Dipengaruhi Budaya

An extended approach of weight collective influence graph for detection influence actor

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