Real-time monitoring of Twitter traffic by using semantic networks

Bisio, Federica; Meda, Claudia; Zunino, Rodolfo; Surlinelli, Roberto; Scillia, Eugenio; Ottaviano, Augusto

doi:10.1145/2808797.2809371

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Computing similarities between users is also an important component in friend recommendation. Many methods can calculate similarities [18,22,23]; examples include common neighbors [14,24], Google PageRank [25], graph-based recommendation [26,27], behavior-based recommendation [28], Pearson correlation coefficient of collaborative filtering [29], content-based recommendation [27,29,30], and so on. For instance, Ref.…”

Section: Discussionmentioning

confidence: 99%

Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

Gong

Gao

Cheng

et al. 2017

Sci. China Inf. Sci.

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Friend recommendation is a fundamental service in both social networks and practical applications, and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend++, a hybrid multi-individual recommendation model that integrates a weighted average method (WAM) into the random walk (RW) framework by seamlessly employing social ties, behavior context, and personal information. In Friend++, the first plus signifies recommending a new friend through network features, while the second plus stands for using node features. To verify our method, we conduct experiments on three social datasets crawled from the Sina microblog system (Weibo). Experimental results show that the proposed method significantly outperforms six baseline methods in terms of recall, precision, F1-measure, and MAP. As a final step, we describe a case study that demonstrates the scalability and universality of our method. Through discussion, we reach a meaningful conclusion: although common interests are more important than user activities in making recommendations, user interactions may be the most important factor in finding the most appropriate potential friends. Keywords multi-individual friend recommendation architecture, behavior context analysis, Intimacy degree, random walk framework, social networks Citation Gong J B, Gao X X, Cheng H, et al. Integrating a weighted-average method into the random walk framework to generate individual friend recommendations.

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Section: Discussionmentioning

confidence: 99%

Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

Gong

Gao

Cheng

et al. 2017

Sci. China Inf. Sci.

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“…The "Support to Law Enforcement agencies' actions" application area is related to providing complete systems architecture, methodologies, and frameworks for agencies dedicated to ensuring compliance with the laws to maintain public security and social welfare. This set is composed of the works by Badii et al [28] that proposed a system architecture to provide data analytics (including a text mining and analytics module) for supporting decision-making in law enforcement agencies; Bisio et al [29] that proposed an approach to allow law enforcement agencies to detect events, using Twitter traffic monitoring, that compromise public security; Basilio et al [30] that presented a methodology to extract knowledge from police reports for extracting information to support activities related to law enforcement; Behmer et al [31] that proposed a framework to support law enforcement agencies in the investigations and analyzes of organized crime; Basilio et al [32] that developed a method for knowledge discovery in emergency response databases based on police reports; and Hou et al [33] that proposed the Bidirectional Encoder Representation from Transformers based on the Chinese relation extraction algorithm for public security, for security information mining.…”

Section: Application Areas For Text Mining In Public Securitymentioning

confidence: 99%

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

De Carvalho,

Dos Santos,

Nepomuceno

et al. 2024

Preprint

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Text mining is a technological trend often highlighted in the continuous exchange of information through interconnected media. Its applicability goes beyond private organizations, as the public sector also requires it to treat textual information regarding services offered. Within this scenario, public security emerges as a prominent user of text mining that seeks to ensure the construction of data and knowledgebases to support decision-making about law enforcement actions to ensure citizen welfare. The primary objectives of this article are: (i) to develop a survey to identify text mining applications, techniques, opportunities, and challenges in public security, and (ii) to outline research directions concerning these topics and provide insights so that interested researchers can develop new studies. The literature was searched within four databases: Scopus, IEEE Xplore, ACM Digital Library, and Web of Science. A filtering process was applied to extract the works most aligned with the target theme, resulting in the selection of 194 of the most relevant works for a literature review. There were identified nineteen key applications of text mining related to public security and the most recurrent techniques and technologies reported between 2014 to 2021, supporting outlining three axes for future directions: one with possible expansion of objectives for new research; another on changes and adaptations in scopes for the methodological context; and the last one on expansions and changes in application scenarios based on the literature.

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“…Different topics within the field of public security have been dealt with from data mining and analysis point-of-view, helping the authorities to monitor situations that can threaten people's security. The study by Bisio et al (2015) on social networks sought to identify unscheduled events on Twitter, based on the service traffic analysis, by fielding an experiment with three different scenarios, two of which were unexpected and involved the security of the participants. They tested techniques based on semantic clustering or text mining and demonstrated the performance metrics for each scenario.…”

Section: Literature Reviewmentioning

confidence: 99%

Public Security Sentiment Analysis on Social Web

Carvalho

Costa

2021

International Journal of Decision Support System Technology

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This article presents (1) the results of a literature review on social web mining and sentiment analysis on public security; (2) the idea of a framework for the analytical process involved in the literature review themes; and (3) a research agenda with a perspective for future studies, considering some elements of the analytical process. The literature review was based on searches of five databases: Scopus, IEEE Xplore, Web of Science, ScienceDirect, and Springer Link. Search strings were applied to retrieve literature material of four kinds, without defining an initial time milestone, to get the historical register of publications associated with the main thematic. After some filtering, primary and secondary findings were separated, enabling the identification of elements for the framework. Finally, the research agenda is presented, containing a set of three research artifacts related to the proposed framework.

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Real-time monitoring of Twitter traffic by using semantic networks

Cited by 8 publications

References 8 publications

Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

Enabling Public Security Text-Based Analytics: A Survey to Outline Research Directions

Public Security Sentiment Analysis on Social Web

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