A Framework to Predict Social Crime through Twitter Tweets By Using Machine Learning

Abbass, Zaheer; Ali, Zain; Ali, Mubashir; Akbar, Bilal; Saleem, Ahsan

doi:10.1109/icsc.2020.00073

Cited by 50 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many of the applications/problems described in the surveyed literature were addressed using fundamental applications of machine learning and/or applied statistics. [17,34,86,149], and classification-based algorithms such as Logistic Regression (LR) [14,32,46], Naive Bayes (NB) [37,51,100,117,121,143], 𝐾-Nearest Neighbours (KNN) [2], Decision Trees (DT) [37,121], Random Forest (RF) [10,90,117,136], Support Vector Machine (SVM) [37,100,117,121], and Artificial Neural Network (ANN) (refer to Section 4.2 for recent trends in the use of neural models).…”

Section: Fundamental Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

2022

View full text Add to dashboard Cite

The rise of social media platforms provides an unbounded, infinitely rich source of aggregate knowledge of the world around us, both historic and real-time, from a human perspective. The greatest challenge we face is how to process and understand this raw and unstructured data, go beyond individual observations and see the “big picture”—the domain of Situation Awareness. We provide an extensive survey of Artificial Intelligence research, focusing on microblog social media data with applications to Situation Awareness, that gives the seminal work and state-of-the-art approaches across six thematic areas: Crime , Disasters , Finance , Physical Environment , Politics , and Health and Population . We provide a novel, unified methodological perspective, identify key results and challenges, and present ongoing research directions.

show abstract

Section: Fundamental Machine Learningmentioning

confidence: 99%

“…Let 𝜖 = (𝑎 𝑖 − 𝑝 𝑖 ) 2 and the notation 𝑥 ≡ 1 𝑛 𝑥 𝑖 . Then the evaluation metrics are defined: 2 where 𝑛 is sample size, and 𝑥 𝑖 , 𝑦 𝑖 are data points, to find a measure of fit for linear regression, and time series problems.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

2022

View full text Add to dashboard Cite

show abstract

“…Recently, social media analysis has been adopted to perform topic based sentiment analysis [18], [19], public opinionmining [20], emotion analysis [21], health surveillance [22], crime monitoring [23], [24], spam detection [25], [26], crisis management [27]- [29], and business marketing [4]. In addition, the social media users are examined for malicious user identification [30]- [33], location inference [34], and influencer identification [35].…”

Section: Related Workmentioning

confidence: 99%

Towards Understanding Trends Manipulation in Pakistan Twitter

Kausar¹,

Tahir²,

Mehmood³

2021

Preprint

View full text Add to dashboard Cite

The rapid adoption of online social media platforms has transformed the way of communication and interaction. On these platforms, discussions in the form of trending topics provide a glimpse of events happening around the world in real-time. Also, these trends are used for political campaigns, public awareness, and brand promotions. Consequently, these trends are sensitive to manipulation by malicious users who aim to mislead the mass audience. In this article, we identify and study the characteristics of users involved in the manipulation of Twitter trends in Pakistan. We propose 'Manipify' -a framework for automatic detection and analysis of malicious users for Twitter trends. Our framework consists of three distinct modules: i) user classifier, ii) hashtag classifier, and ii) trend analyzer. The user classifier introduces a novel approach to automatically detect manipulators using tweet content and user behaviour features. Also, the module classifies human and bot users. Next, the hashtag classifier categorizes trending hashtags into six categories assisting in examining manipulators behaviour across different categories. Finally, the trend analyzer module examines users, hashtags, and tweets for hashtag reach, linguistic features and user behaviour. Our user classifier module achieves 0.91 accuracy in classifying the manipulators. We further test Manipify on the dataset comprising of 665 trending hashtags with 5.4 million tweets and 1.9 million users. The analysis of trends reveals that the trending panel is mostly dominated by political hashtags. In addition, our results show a higher contribution of human accounts in trend manipulation as compared to bots. Furthermore, we present two case studies of hashtag-wars and anti-state propaganda to implicate the real-world application of our research.

show abstract

“…Furthermore, in data mining process, the transactional datasets are exploited to generate the correlations at high level. 46 The data pre-processing can be achieved by employing following steps: data normalization, data discretization, scrapping, and cleansing processes as well as dimensionality minimization that is discussed in the following section.…”

Section: Data Pre-processingmentioning

confidence: 99%

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

Lakshmi¹,

Krishnamurthy²

2021

Concurrency and Computation

View full text Add to dashboard Cite

Nowadays, the concept of data mining is employed widely and created a great deal of attention due to its fast arrival. Numerous approaches to frequent itemsets and association rule mining (ARM) are exemplified in recent years, but still, the performances based on scalability and processing time are considered as a major drawback that results in obtaining the solutions with very poor quality. To overcome such shortcomings, this article proposes three significant phases, namely, the data pre-processing phase, data pre-processing, frequent itemset mining, and ARM. In data pre-processing phase, the collected twitter datasets are pre-processed to eliminate redundant data and convert them into an appropriate format for further mining. In the frequent itemset mining phase, an Apriori algorithm is employed for the exact mining of frequent itemsets. The ARM phase utilizes the fuzzy manta ray foraging (FMRF) optimization algorithm that involves the generation of association rules from the huge itemsets thereby achieving minimum confidence and minimum support value. Here, the recent tweets regarding Covid-19, trump2020, joebiden, draintheswamp, and Godzilla are the datasets collected from the Twitter web link. The experimental analysis and the comparative performances are performed for various simulation measures and the results reveal that the proposed approach provides effective performances when compared with various other existing approaches.

show abstract

A Framework to Predict Social Crime through Twitter Tweets By Using Machine Learning

Cited by 50 publications

References 20 publications

Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

Socially Enhanced Situation Awareness from Microblogs Using Artificial Intelligence: A Survey

Towards Understanding Trends Manipulation in Pakistan Twitter

Association rule mining based fuzzy manta ray foraging optimization algorithm for frequent itemset generation from social media

Contact Info

Product

Resources

About