The Impact of the Collective Influence of Search Engines on Social Networks

Kong, Dezhang; Fu, Cai; Yang, Jiakuan; Xu, Deliang; Han, Lei

doi:10.1109/access.2017.2767075

Cited by 7 publications

(3 citation statements)

References 28 publications

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“…Furthermore, Teng et al [27] proposed developing the CI method by adding a Linear Threshold Model (LTM). Another researcher by Kong et al [28] introduced the concept of the probably-established subcritical path (PSP) to determine the distribution of information based on the path traversed by nodes. The results showed that PSP-based CI performed better than CI-TM.…”

Section: A R T I C L E I N F O Abstractmentioning

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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Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

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

Martono

Azhari

Mustofa

2022

Int. J. Adv. Intell. Informatics

View full text Add to dashboard Cite

show abstract

“…To use the automatically labeled data from distance supervision for BERT effectively, we constructed post- 1 aiopen.etri.re.kr training with the large-sized automatically labeled data before fine-tuning using the same objective as NER. Although the automatically labeled data are noisy data with some errors, they can contribute to NER's improvement by their usage of post-training before fine-tuning.…”

Section: ) Post-training Using Automatically Labeled Datamentioning

confidence: 99%

“…SMM app data has provided important clues and evidence in legal trials and criminal investigations. Thus, it is crucial to study the extraction of information from these chat data [1].…”

Section: Introductionmentioning

confidence: 99%