Understanding the Language of ISIS: An Empirical Approach to Detect Radical Content on Twitter Using Machine Learning

Cost-efficient single-polarized X-band radars are a feasible alternative due to their high sensitivity and resolution, which makes them well suited for complex precipitation patterns. The first horizontal scanning weather radar in Peru was installed in Piura in 2019, after the devastating impact of the 2017 coastal El Niño. To obtain a calibrated rain rate from radar reflectivity, we employ a modified empirical approach and draw a direct comparison to a well-established machine learning technique used for radar QPE. For both methods, preprocessing steps are required, such as clutter and noise elimination, atmospheric, geometric, and precipitation-induced attenuation correction, and hardware variations. For the new empirical approach, the corrected reflectivity is related to rain gauge observations, and a spatially and temporally variable parameter set is iteratively determined. The machine learning approach uses a set of features mainly derived from the radar data. The random forest (RF) algorithm employed here learns from the features and builds decision trees to obtain quantitative precipitation estimates for each bin of detected reflectivity. Both methods capture the spatial variability of rainfall quite well. Validating the empirical approach, it performed better with an overall linear regression slope of 0.65 and r of 0.82. The RF approach had limitations with the quantitative representation (slope = 0.44 and r = 0.65), but it more closely matches the reflectivity distribution, and it is independent of real-time rain-gauge data. Possibly, a weighted mean of both approaches can be used operationally on a daily basis.

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Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9040217 ◽

2020 ◽

Vol 9 (4) ◽

pp. 217

Author(s):

Yuxue Wang ◽

Su Li ◽

Xun Zhang ◽

Dong Jiang ◽

Mengmeng Hao ◽

...

Keyword(s):

Machine Learning ◽

Site Selection ◽

False Positive Rate ◽

Empirical Approach ◽

Spatial Accessibility ◽

Population Census ◽

Learning Approaches ◽

Digital Signage ◽

Ring Road ◽

Positive Rate

With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor data and combines empirical location models with machine learning methods to recommend locations for digital signage. The outdoor commercial digital signage within the Sixth Ring Road area in Beijing was selected as an example and was combined with population census, average house prices, social network check-in data, the centrality of traffic networks, and point of interest (POI) facilities data as research data. The data were divided into 100–1000 m grids for digital signage site-selection modelling. The empirical approach of the improved Huff model was used to calculate the spatial accessibility of digital signage, and machine learning approaches such as back propagation neural network (BP neural networks) were used to calculate the potential location of digital signage. The site of digital signage to be deployed was obtained by overlay analysis. The result shows that the proposed method has a higher true positive rate and a lower false positive rate than the other three site selection models, which indicates that this method has higher accuracy for site selection. The site results show that areas suitable for digital signage are mainly distributed in Sanlitun, Wangfujing, Financial Street, Beijing West Railway Station, and along the main road network within the Sixth Ring Road. The research provides a reference for integrating geographical features and content data into the site-selection algorithm. It can effectively improve the accuracy and scientific nature of digital signage layouts and the efficiency of digital signage to a certain extent.

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An Improved Sentiment Analysis Approach to Detect Radical Content on Twitter

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2021100103 ◽

2021 ◽

Vol 16 (4) ◽

pp. 52-73

Author(s):

kamel Ahsene Djaballah ◽

Kamel Boukhalfa ◽

Omar Boussaid ◽

Yassine Ramdane

Keyword(s):

Machine Learning ◽

Data Mining ◽

Social Networks ◽

Fuzzy Logic ◽

Sentiment Analysis ◽

Experimental Results ◽

Data Mining Algorithms ◽

Terrorist Groups ◽

Radical Content ◽

Mining Algorithms

Social networks are used by terrorist groups and people who support them to propagate their ideas, ideologies, or doctrines and share their views on terrorism. To analyze tweets related to terrorism, several studies have been proposed in the literature. Some works rely on data mining algorithms; others use lexicon-based or machine learning sentiment analysis. Some recent works adopt other methods that combine multi-techniques. This paper proposes an improved approach for sentiment analysis of radical content related to terrorist activity on Twitter. Unlike other solutions, the proposed approach focuses on using a dictionary of weighted terms, the Word2vec method, and trigrams, with a classification based on fuzzy logic. The authors have conducted experiments with 600 manually annotated tweets and 200,000 automatically collected tweets in English and Arabic to evaluate this approach. The experimental results revealed that the new technique provides between 75% to 78% of precision for radicality detection and 61% to 64% to detect radicality degrees.

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Non-empirical problems in fair machine learning

Ethics and Information Technology ◽

10.1007/s10676-021-09608-9 ◽

2021 ◽

Author(s):

Teresa Scantamburlo

Keyword(s):

Machine Learning ◽

Decision Making ◽

Predictive Accuracy ◽

Higher Order ◽

Empirical Approach ◽

Current Debate ◽

New Techniques ◽

Trade Offs ◽

Conceptual Problems ◽

Empirical Framework

AbstractThe problem of fair machine learning has drawn much attention over the last few years and the bulk of offered solutions are, in principle, empirical. However, algorithmic fairness also raises important conceptual issues that would fail to be addressed if one relies entirely on empirical considerations. Herein, I will argue that the current debate has developed an empirical framework that has brought important contributions to the development of algorithmic decision-making, such as new techniques to discover and prevent discrimination, additional assessment criteria, and analyses of the interaction between fairness and predictive accuracy. However, the same framework has also suggested higher-order issues regarding the translation of fairness into metrics and quantifiable trade-offs. Although the (empirical) tools which have been developed so far are essential to address discrimination encoded in data and algorithms, their integration into society elicits key (conceptual) questions such as: What kind of assumptions and decisions underlies the empirical framework? How do the results of the empirical approach penetrate public debate? What kind of reflection and deliberation should stakeholders have over available fairness metrics? I will outline the empirical approach to fair machine learning, i.e. how the problem is framed and addressed, and suggest that there are important non-empirical issues that should be tackled. While this work will focus on the problem of algorithmic fairness, the lesson can extend to other conceptual problems in the analysis of algorithmic decision-making such as privacy and explainability.

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Evaluating machine learning classification for financial trading: An empirical approach

Expert Systems with Applications ◽

10.1016/j.eswa.2016.01.018 ◽

2016 ◽

Vol 54 ◽

pp. 193-207 ◽

Cited By ~ 45

Author(s):

Eduardo A. Gerlein ◽

Martin McGinnity ◽

Ammar Belatreche ◽

Sonya Coleman

Keyword(s):

Machine Learning ◽

Empirical Approach ◽

Machine Learning Classification ◽

Financial Trading

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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