A new cluster computing technique for social media data analysis

Xu, Qingzhen; Li, Miao

doi:10.1007/s10586-017-1436-9

Cited by 18 publications

(4 citation statements)

References 20 publications

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“…The real number of connections e i was the degree of interconnection between nodes in Facebook network of i ; this was calculated by dividing the number of mutual friends between transmitters and each of their friends, and then dividing the result by 2 to avoid recalculating the mutual relationship between i and n 1 or i and n 2 in the data collected by the application (Xu and Li, 2019). Thus, e i was formally defined using the adjacency matrix J , as shown in Eq.…”

Section: Methodsmentioning

confidence: 99%

The influence of public health information sharing on social media during the early COVID-19 outbreak

Chen

Chiu

2021

DTA

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PurposeSocial media have become famous platform to search and share the COVID-19-related information. The objective of this research is to bridge the gap by proposing the effects of network cluster and transmitter activity on information sharing process.Design/methodology/approachData were collected by using Facebook application, which was available for 14 days (May 1–14) in 2020. These data were analyzed to determine the influence of the network cluster and transmitter activity.FindingsThe results showed that network cluster is positively related to transmitter activity on social media. In addition, transmitter activity partially mediated the effect of network cluster on the extent of information liked and shared. That is, transmitter activity can affect COVID-19-related information sharing on Facebook, and the activity effect is plausible and should become stronger as social network become denser.Originality/valueThis study has contributed to the knowledge of health information sharing in social media and has generated new opportunities for research into the role of network cluster. As social media is firmly entrenched in society, researches that improve the experience or quality for users is potentially impactful.

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Section: Methodsmentioning

confidence: 99%

The influence of public health information sharing on social media during the early COVID-19 outbreak

Chen

Chiu

2021

DTA

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“…Working with conventional, locally based computer systems can make this challenging, let alone cloud datacenters, which may be thousands of miles away, run 40,000 virtual machines (VMs) across 512 servers, and are accessed by 1000 tenants, of which the accessed is one. This may just be too much information for the ordinary juror to process given that they only have a basic grasp of how to utilise a home computer [73][74][75].…”

Section: Digital Cloud Forensicsmentioning

confidence: 99%

Cloud Computing Forensics; Challenges and Future Perspectives: A Review

Cinar,

Bharadiya

2023

AJRCoS

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Cloud computing has become increasingly popular in recent years, evolving into a computing paradigm that is both cost-effective and efficient. It has the potential to be one of the technologies that has had the most significant impact on computing throughout its history. Regrettably, cloud service providers and their customers have not yet developed major forensic tools that can assist with the investigation of criminal conduct that occurs in the cloud. Because it is difficult to prevent cloud vulnerabilities and criminal targeting, it is necessary to be aware of how digital forensic investigations of the cloud may be carried out. This is because cloud vulnerabilities and criminal targeting are difficult to avoid. In this context, the current study examines current and future trends in cloud forensics, methodology for cloud forensics, and cloud forensic tools. In addition, the study also looks at cloud forensic approaches.

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“…To solve this problem, literature [19] introduces the concept of "generation" in the operation of the single-pass algorithm, inputting the document set in batches, clustering each batch of documents first, and then clustering the initial clustering results with the existing topic clusters, which effectively alleviates the order-sensitive problem of the single-pass algorithm's order-sensitive problem but makes the clustering results affected by the preliminary clustering process. In literature [20], based on the K-means clustering algorithm, the canopy algorithm is introduced to initialize the data, and the results of the algorithm are continuously updated by combining the hood center in the canopy algorithm and the class cluster center in the K-means algorithm, while the parallelized operation of the canopy-k-means algorithm is realized based on the Hadoop platform. e topic clustering results obtained in literature [21] based on this scheme are less affected by the input order of news data but still need to set the number of topics in advance, which is difficult to predict accurately in the complex Internet environment.…”

Section: Complexitymentioning

confidence: 99%

Digital Media Hotspot Mining Algorithm Implementation with Complex Systems in the Mobile Internet Environment

Jia

Tsai

2021

Complexity

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With the development of the Internet, the amount of information present on the network has grown rapidly, leading to increased difficulty in obtaining effective information. Especially for individuals, enterprises, and institutions with a large amount of information, it is an almost impossible task to integrate and analyze Internet information with great difficulty just by human resources. Internet hot events mining and analysis technology can effectively solve the above problems by alleviating information overload, integrating redundant information, and refining core information. In this paper, we address the above problems and research hot event topic sentence generation techniques in the field of hot event mining and design a hybrid event candidate set construction algorithm based on topic core word mapping and event triad selection. The algorithm uses the PAT-Tree technique to extract high-frequency core words in topic hotspots and maps the high-frequency words into sentences to generate a part of event core sentences. The other part of event core sentences is extracted from the topic hotspots by making event triples as candidate elements, and sentences containing event elements are extracted from the topic hotspots. The sets of event core sentences generated by the two methods are mixed and filtered and sorted to obtain the candidate set, which can be used to build a word graph-based main service channel (MSC) model. In this paper, we also propose an improved word graph-based MSC model and use it for the extraction of event topic sentences. Based on the above research, a hot event analysis system is implemented. The system analyzes the existing topic data and uses the event topic sentence generation algorithm studied in this paper to generate the titles of hot spots, that is, hot events. At the same time, the topics are displayed from different dimensions, and data visualization is completed. The visualization includes the trend change of event hotness, trend change of event sentiment polarity, and distribution of event article sources.

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A new cluster computing technique for social media data analysis

Cited by 18 publications

References 20 publications

The influence of public health information sharing on social media during the early COVID-19 outbreak

The influence of public health information sharing on social media during the early COVID-19 outbreak

Cloud Computing Forensics; Challenges and Future Perspectives: A Review

Digital Media Hotspot Mining Algorithm Implementation with Complex Systems in the Mobile Internet Environment

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