Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

Hernandez-Suarez, Aldo; Toscano-Medina, Karina; Martínez-Hernández, V.; Olivares-Mercado, Jesús; Sánchez, Vı́ctor

doi:10.3390/s18051380

Cited by 58 publications

(38 citation statements)

References 36 publications

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“…The solution was developed in Python 3 programming language, using Scrapy-An open-source framework [50]. Previous studies [51,52] have used similar technologies to collect big volumes of data automatically in the absence of an API (Application Programming Interface). Article [53] describes in detail the process of data acquisitions, difficulties encountered and solutions to solve them.…”

Section: Data Gathering Processmentioning

confidence: 99%

Skill Needs for Early Career Researchers—A Text Mining Approach

et al. 2019

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Research and development activities are one of the main drivers for progress, economic growth and wellbeing in many societies. This article proposes a text mining approach applied to a large amount of data extracted from job vacancies advertisements, aiming to shed light on the main skills and demands that characterize first stage research positions in Europe. Results show that data handling and processing skills are essential for early career researchers, irrespective of their research field. Also, as many analyzed first stage research positions are connected to universities, they include teaching activities to a great extent. Management of time, risks, projects, and resources plays an important part in the job requirements included in the analyzed advertisements. Such information is relevant not only for early career researchers who perform job selection taking into account the match of possessed skills with the required ones, but also for educational institutions that are responsible for skills development of the future R&D professionals.

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Section: Data Gathering Processmentioning

confidence: 99%

Skill Needs for Early Career Researchers—A Text Mining Approach

et al. 2019

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“…Consequently, organizations are able to make improvements and adopt practices in line with the opinion of their target audience. Online platforms such as Twitter, which generate large amounts of data all the time—constituting a Big Data producer—have the potential to facilitate research over social phenomena based on sentiment analysis [ 5 ], as well as the search for new solutions to help extract useful knowledge from those large datasets.…”

Section: Introductionmentioning

confidence: 99%

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

Júnior

Albuquerque

Andrade

et al. 2020

Sensors

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Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation.

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“…Event prediction and monitoring can then be carried out by applying connective action theory that links a live event with the reactions of users [19]. For example, it has been demonstrated that events with a negative impact on society can motivate hacker activists to perpetrate cyber attacks [20]. Twitter can then be used as an alternative engine for exchanging information related to natural disasters, such as fires, floods, hurricanes, and earthquakes.…”

Section: Introductionmentioning

confidence: 99%

Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation

Hernandez-Suarez

Sánchez-Pérez

Toscano-Medina

et al. 2019

Sensors

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In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico’s 2017 Earthquake is presented, and the data extracted during and after the event are reported.

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Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization

Cited by 58 publications

References 36 publications

Skill Needs for Early Career Researchers—A Text Mining Approach

Skill Needs for Early Career Researchers—A Text Mining Approach

Anonymous Real-Time Analytics Monitoring Solution for Decision Making Supported by Sentiment Analysis

Using Twitter Data to Monitor Natural Disaster Social Dynamics: A Recurrent Neural Network Approach with Word Embeddings and Kernel Density Estimation

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