Ranking of Importance Measures of Tweet Communities: Application to Keyword Extraction From COVID-19 Tweets in Japan

Harakawa, Ryosuke; Iwahashi, Masahiro

doi:10.1109/tcss.2021.3063820

Cited by 9 publications

(5 citation statements)

References 38 publications

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“…As in the study 27 , we performed pre-processing for each tweet. Concretely, using a natural language processing tool called Janome ( https://mocobeta.github.io/janome/en/ ), we performed morphological analysis 28 and extracted only the nouns.…”

Section: Resultsmentioning

confidence: 99%

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Trend clustering from COVID-19 tweets using graphical lasso-guided iterative principal component analysis

Harakawa

Ito

Iwahashi

2022

Sci Rep

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This article presents a method for trend clustering from tweets about coronavirus disease (COVID-19) to help us objectively review the past and make decisions about future countermeasures. We aim to avoid detecting usual trends based on seasonal events while detecting essential trends caused by the influence of COVID-19. To this aim, we regard daily changes in the frequencies of each word in tweets as time series signals and define time series signals with single peaks as target trends. To successfully cluster the target trends, we propose graphical lasso-guided iterative principal component analysis (GLIPCA). GLIPCA enables us to remove trends with indirect correlations generated by other essential trends. Moreover, GLIPCA overcomes the difficulty in the quantitative evaluation of the accuracy of trend clustering. Thus, GLIPCA’s parameters are easier to determine than those of other clustering methods. We conducted experiments using Japanese tweets about COVID-19 from March 8, 2020, to May 7, 2020. The results show that GLIPCA successfully distinguished trends before and after the declaration of a state of emergency on April 7, 2020. In addition, the results reveal the international argument about whether the Tokyo 2020 Summer Olympics should be held. The results suggest the tremendous social impact of the words and actions of Japanese celebrities. Furthermore, the results suggest that people’s attention moved from worry and fear of an unknown novel pneumonia to the need for medical care and a new lifestyle as well as the scientific characteristics of COVID-19.

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Section: Resultsmentioning

confidence: 99%

“…Concretely, for each trend , we select k trends from in descending order of cosine similarities to construct an unweighted network. We set k to 3 as in the study 27 . Note that the calculated network includes indirect correlations.…”

Section: Methodsmentioning

confidence: 99%

Trend clustering from COVID-19 tweets using graphical lasso-guided iterative principal component analysis

Harakawa

Ito

Iwahashi

2022

Sci Rep

Self Cite

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“…Representative results include: Literature. [16] studied the evaluation method of microblog community influence, proposed an index system based on microblog information dissemination mechanism, combined quantitative indicators and qualitative indicators, and used principal component analysis to connect these the indicators combined into several comprehensive hands, which simplifies the indicator system; Literature [17] proposed a community impact evaluation model framework from the perspective of information dissemination and a set of related definitions of community impact evaluation forms, involving user impact and community impact; Literature [18] proposed a variable influence community detection method based on PageRank, which can adjust the community where a specific node is located and increase its influence. However, for the target community discovery task, in addition to accurately mining the community composed of high-quality nodes similar to the sample nodes given by the user, the ability of the community to spread the internal information of the community to external users, that is, the external influence of the community is also a significant factor.…”

Section: Quantification Methods Of Community External Influencementioning

confidence: 99%

Social Theory Aware Influence Maximization Approach for Target Community Detection

Scholar

Professor

2023

Preprint

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Targeted community detection is a network analysis involving identifying specific subsets or clusters of nodes (communities) within a more extensive network that are particularly relevant to a topic or phenomenon. Unlike traditional community detection methods, which aim to identify all communities within a network, targeted community detection methods focus on finding a specific community or set of communities based on some pre-defined criteria. Nevertheless, all previous effort either mainly disregards the external effect of the community or is not ”goal-based,” i.e., inappropriate for the goal request. To address this issue, we present an attribute network-oriented target community discovery technique that blends user interest preferences and community influence to mine high-quality communities associated with user preferences and have the most influence. To capture the attribute subspace weight of the possible target community and mine user preferences, the greatest k-cluster, including sample nodes, is first mined as the core of the potential target community by synthesising the node structure and attribute information. Then the fusion of the maximum k-cluster with the sample nodes is mined as the core of the potential target community. Finally, the fusion of the sample nodes defines the community’s external influence score quantifica-tion technique and combines the community quality function value with the exterior influence score. Finally, all possible target communities are ranked by their influence score. As a result, the communities with the highest overall quality become the target communities. In addition, a 2-fold pruning procedure is intended to increase the method’s performance and efficiency while calculating the attribute subspace weights of the largest k-cluster. Experimental results on synthetic and actual network datasets validate the efficiency and efficacy of the suggested strategy.

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“…There are many examples of ranking data in an array of academic disciplines, including education (Acuna-Soto et al , 2021) psychology (Regenwetter and Rykhlevskaia, 2007), quality of life (Peiro-Palomino and Picazo-Tadeo, 2018), sociology (Harakawa and Iwahashi, 2021).…”

Section: Phasementioning

confidence: 99%

Application of grounded theory in construction of factors of internal efficiency and external effectiveness of cyber security and developing impact models

Dube

Mohanty²

2022

OCJ

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PurposeAs evident from the literature review, the research on cyber security performance is centered on security metrics, maturity models, etc. Essentially, all these are helpful for evaluating the efficiency of cyber security organization but what matters is how the factors of internal efficiency affect the business performance, i.e. the external effectiveness. The purpose of this research paper is to derive the factors of internal efficiency and external effectiveness of cyber security and develop impact model to identify the most and least preferred parameters of internal efficiency with respect to all the parameters of external effectiveness.Design/methodology/approachThere are two objectives for this research: Deriving the factors of internal efficiency and external effectiveness of cyber security; Developing a model to identify the impact of internal efficiency factors on the external effectiveness of cyber security since there is not much evidence of research in defining the factors of internal efficiency and external effectiveness of cyber security, the authors have chosen grounded theory methodology (GTM) to derive the parameters. In this study emic approach of GTM is followed and an algorithm is developed for administering the grounded theory research process. For the second research objective survey methodology and rank order was used to formulate the impact model. Two different samples and questionnaires were designed for each of the objectives.FindingsFor the objective 1, 11 factors of efficiency and 10 factors of effectiveness were derived. These are used as independent and dependent variable respectively in the later part of the research for the second objective. For the objective 2 the impact models among independent and dependent variables were formulated to find out the following. Most and least preferred parameters lead to internal efficiency of cyber security organization to identify the most and least preferred parameters of internal efficiency with respect to all the parameters external effectiveness.Research limitations/implicationsThe factors of internal efficiency and external effectiveness constructed by using grounded theory cannot remain constant in the long run, because of dynamism of the domain itself. Over and above this, there are inherent limitations of the tools like grounded theory, used in the research. Few important limitations of GTM are as below in grounded theory, it is comparatively difficult to maintain and demonstrate the rigors of research discipline. The sheer volume of data makes the analysis and interpretation complex, and lengthy time consuming. The researchers’ presence during data gathering, which is often unavoidable and desirable too in qualitative research, may affect the subjects’ responses. The subjectivity of the data leads to difficulties in establishing reliability and validity of approaches and information. It is difficult to detect or to prevent researcher-induced bias.Practical implicationsThe internal efficiency and external effectiveness factors of cyber security can be further correlated by the future researchers to understand the correlations among all the factors and predict cyber security performance. The grounded theory algorithm developed by us can be further used for qualitative research for deriving theory through abstractions in the areas where there is no sufficient availability of data. Practitioners of cyber security can use this research to focus on relevant areas depending on their respective business objective/requirements. The models developed by us can be used by the future researchers to for various sectoral validations and correlations.Social implicationsThough the financial costs of a cyber-attack are steep, the social impact of cyber security failures is less readily apparent but can cause lasting damage to customers, employees and the company. Therefore, it is always important to be mindful of how the impact of cyber security affects society as well as the bottom line when they are calculating the potential impact of a breach. Underestimating either impact can destroy a brand. The factor of internal efficiency and external effectiveness derived by us will help stakeholder in focusing on relevant area depending on their business. The impact model developed in this research is very useful for focusing a particular business requirement and accordingly tune the efficiency factor.Originality/valueDuring literature study the authors did not find any evidence of application of grounded theory approach in cyber security research. While the authors were exploring research literature to find out some insight into the factor of internal efficiency and external effectiveness of cyber security, the authors did not find concrete and objective research on this. This motivated us to use grounded theory to derive these factors. This, in the authors’ opinion is one of the pioneering and unique contribution to the research as to the authors’ knowledge no researchers have ever tried to use this methodology for the stated purpose and cyber security domain in general. In this process the authors have also developed an algorithm for administering GTM. Further developing impact models using factors of internal efficiency and external effectiveness has lots of managerial and practical implication.

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Ranking of Importance Measures of Tweet Communities: Application to Keyword Extraction From COVID-19 Tweets in Japan

Cited by 9 publications

References 38 publications

Trend clustering from COVID-19 tweets using graphical lasso-guided iterative principal component analysis

Trend clustering from COVID-19 tweets using graphical lasso-guided iterative principal component analysis

Social Theory Aware Influence Maximization Approach for Target Community Detection

Application of grounded theory in construction of factors of internal efficiency and external effectiveness of cyber security and developing impact models

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