Supervised Feature Based Malicious Profile Detection System in Online Social Media

2019 ◽  
Vol 7 (6) ◽  
pp. 164-171
Author(s):  
Ms. Anamika Joshi ◽  
Dr. D.S. Bhilare
Keyword(s):  
Social Media ◽  
Detection System ◽  
Online Social Media ◽  
Feature Based

scholarly journals Crisis-DIAS: Towards Multimodal Damage Analysis - Deployment, Challenges and Assessment

2020 ◽  
Vol 34 (01) ◽  
pp. 346-353 ◽  
Author(s):  
Mansi Agarwal ◽  
Maitree Leekha ◽  
Ramit Sawhney ◽  
Rajiv Ratn Shah
Keyword(s):  
Social Media ◽  
Damage Assessment ◽  
Damage Identification ◽  
Detection System ◽  
Learning Performance ◽  
Social Media Data ◽  
Online Social Media ◽  
Assessment Performance ◽  
The Impact ◽  
Media Data

In times of a disaster, the information available on social media can be useful for several humanitarian tasks as disseminating messages on social media is quick and easily accessible. Disaster damage assessment is inherently multi-modal, yet most existing work on damage identification has focused solely on building generic classification models that rely exclusively on text or image analysis of online social media sessions (e.g., posts). Despite their empirical success, these efforts ignore the multi-modal information manifested in social media data. Conventionally, when information from various modalities is presented together, it often exhibits complementary insights about the application domain and facilitates better learning performance. In this work, we present Crisis-DIAS, a multi-modal sequential damage identification, and severity detection system. We aim to support disaster management and aid in planning by analyzing and exploiting the impact of linguistic cues on a unimodal visual system. Through extensive qualitative, quantitative and theoretical analysis on a real-world multi-modal social media dataset, we show that the Crisis-DIAS framework is superior to the state-of-the-art damage assessment models in terms of bias, responsiveness, computational efficiency, and assessment performance.

scholarly journals Heterogeneous Ensemble Structure based Universal Spam Profile Detection System for Social Media Networks

2020 ◽  
Vol 9 (1) ◽  
pp. 1028-1039
Keyword(s):  
Social Media ◽  
Detection System ◽  
User Profile ◽  
Classification Model ◽  
Support Vector ◽  
Social Media Networks ◽  
Online Social Media ◽  
Linear Polynomial ◽  
Proposed Model ◽  
Social Media Network

The exponential rise in internet technology and online social media networks have revitalized human-being to connect and socialize globally irrespective of geographical and any demographic boundaries. Additionally, it has revitalized business communities to reach target audiences through social media networks. However, as parallel adverse up-surge the everincreasing presence of malicious users or spam has altered predominant intend of such social media network by propagating biased contents, malicious contents and fraud acts. Avoiding and neutralizing such malefic users on social media network has remained a critical challenge due to gigantically large size and user’s diversity such as Facebook, Twitter, and LinkedIn etc. Though exploiting certain user’s behavior and content types can help identifying malicious users, majority of the existing methods are limited due to confined parametric assessment, and inferior classification approaches. With intend to provide spam profile detection system in this paper a novel heterogeneous ensemblebased method is developed. The proposed model exploits user profile features, user’s activity features, location features and content features to perform spam user profile detection. To ensure optimality of computational significances, we applied multi-phased feature selection method employing Wilcoxon Rank Sum test, Significant Predictor test, and Pearson Correlation test, which assured retaining optimal feature sets for further classification. Subsequently, applying an array of machine learning methods, including Logistic regression, decision tree, Support Vector Machine variants with Linear, Polynomial and RBF kernels, Least Square SVM with linear, polynomial and RBF kernels, ANN with different kernels, etc we constituted a robust ensemble model for spam user profile classification. Simulations revealed that the proposed ensemble classification model achieves accuracy and F-score higher than 98%, which is the highest amongst major works done so far. It affirms suitability and robustness of the proposed model for real time spam profile detection and classification on social media platforms

Impact of Online Social Media on Consumer Decision Making: A Study in Dehradun

2012 ◽  
Vol 3 (5) ◽  
pp. 379-381
Author(s):  
Dr. Aruna Kumar Mishra ◽  
◽  
Narendra Kumar Narendra Kumar ◽  
Abhishek Sharma
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Consumer Decision Making ◽  
Consumer Decision ◽  
Online Social Media

DERADICALISATION IDEAS IN SOCIAL MEDIA THROUGH THE RELIGIOUS-MULTICULTURAL THERAPY APPROACH TO THE DA'WAH CULTURE

2020 ◽  
Vol 24 (1) ◽  
pp. 58
Author(s):  
Anwar Hafidzi
Keyword(s):  
Social Media ◽  
Real World ◽  
Environmental Conditions ◽  
Islamic Law ◽  
Research Tool ◽  
Therapy Approach ◽  
The Real ◽  
Multicultural Therapy ◽  
Online Social Media ◽  
Library Research

This research begins with an understanding of the endemic radicalism of society, not only of the real world, but also of various online social media. This study showed that the avoidance of online radicalism can be stopped as soon as possible by accusing those influenced by the radical radicality of a secular religious approach. The methods used must be assisted in order to achieve balanced understanding (wasathiyah) under the different environmental conditions of the culture through recognizing the meaning of religion. The research tool used is primarily library work and the journal writings by Abu Rokhmad, a terrorist and radicalise specialist. The results of this study are that an approach that supports inclusive ism will avoid the awareness of radicalization through a heart-to-heart approach. This study also shows that radical actors will never cease to argue dramatically until they are able to grasp different views from Islamic law, culture, and families.Keywords: radicalism, deradicalization, multiculturalism, culture, religion, moderate.Penelitian ini berawal dari paham radikalisme yang telah mewabah di masyarakat, bukan hanya di dunia nyata, bahkan sudah menyusup di berbagai media sosial online. Penelitian ini menemukan bahwa cara menangkal radikalisme online dapat dilakukan pencegahan sedini mungkin melalui pendekatan konseling religius multikultural terhadap mereka yang terkena paham radikal radikal. Diantara teknik yang digunakan adalah melalui pemahaman tentang konsep agama juga perlu digalakkan agar memunculkan pemahaman yang moderat (wasathiyah) diberbagai keadaan lingkungan masyarakat. Metode yang digunakan untuk penelitian ini adalah library research dengan sumber utama adalah karya dan jurnal karya Abu Rokhmad seorang pakar dalam masalah terorisme dan radikalisme. Temuan penelitian ini adalah paham radikalisasi itu dapat dihentikan dengan pendekatan hati ke hati dengan mengedepankan budaya yang multikultural. Kajian ini juga membuktikan bahwa pelaku paham radikal tidak akan pernah berhenti memberikan argumen radikal kecuali mampu memahami perbedaan pendapat yang bersumber dari syariat Islam, lingkungan sosial, dan keluarga.Kata kunci: radikalisme, deradikalisasi, multikultural, budaya, agama, moderat.

Customer Revenue Sharing Program in Online Social Media

2012 ◽  
Author(s):  
Fouad H. Mirzaei ◽  
Fredrik Odegaard ◽  
Xinghao Yan
Keyword(s):  
Social Media ◽  
Revenue Sharing ◽  
Online Social Media

Understanding the Role of Online Media Platforms in Airport Capital Projects and Community Outreach

2021 ◽  
pp. 036119812199347
Author(s):  
Max Z. Li ◽  
Megan S. Ryerson
Keyword(s):  
Social Media ◽  
Best Practices ◽  
Community Outreach ◽  
Capital Projects ◽  
The Public ◽  
Online Social Media ◽  
Social Media Platforms ◽  
Project Phases ◽  
Media Channels ◽  
Common Content

Community outreach and engagement efforts are critical to an airport’s role as an ever-evolving transportation infrastructure and regional economic driver. As online social media platforms continue to grow in both popularity and influence, a new engagement channel between airports and the public is emerging. However, the motivations behind and effectiveness of these social media channels remain unclear. In this work, we address this knowledge gap by better understanding the advantages, impact, and best practices of this newly emerging engagement channel available to airports. Focusing specifically on airport YouTube channels, we first document quantitative viewership metrics, and examine common content characteristics within airport YouTube videos. We then conduct interviews and site visits with relevant airport stakeholders to identify the motivations and workflow behind these videos. Finally, we facilitate sample focus groups designed to survey public perceptions of the effectiveness and value of these videos. From our four project phases, to maximize content effectiveness and community engagement potential, we synthesize the following framework of action items, recommendations, and best practices: (C) Consistency and community; (O) Organizational structure; (M) Momentum; (B) Branding and buy-in; (A) Activity; (T) Two-way engagement; (E) Enthusiasm; and (D) Depth, or as a convenient initialism, our COMBATED framework.

A Survey of Computational Methods for Online Mental State Assessment on Social Media

2021 ◽  
Vol 2 (2) ◽  
pp. 1-31
Author(s):  
Esteban A. Ríssola ◽  
David E. Losada ◽  
Fabio Crestani
Keyword(s):  
Social Media ◽  
Mental Disorders ◽  
Mental State ◽  
Extraction Process ◽  
State Assessment ◽  
Future Research ◽  
Linguistic Markers ◽  
Online Social Media ◽  
Suicide Behaviour ◽  
Social Media Platforms

Mental state assessment by analysing user-generated content is a field that has recently attracted considerable attention. Today, many people are increasingly utilising online social media platforms to share their feelings and moods. This provides a unique opportunity for researchers and health practitioners to proactively identify linguistic markers or patterns that correlate with mental disorders such as depression, schizophrenia or suicide behaviour. This survey describes and reviews the approaches that have been proposed for mental state assessment and identification of disorders using online digital records. The presented studies are organised according to the assessment technology and the feature extraction process conducted. We also present a series of studies which explore different aspects of the language and behaviour of individuals suffering from mental disorders, and discuss various aspects related to the development of experimental frameworks. Furthermore, ethical considerations regarding the treatment of individuals’ data are outlined. The main contributions of this survey are a comprehensive analysis of the proposed approaches for online mental state assessment on social media, a structured categorisation of the methods according to their design principles, lessons learnt over the years and a discussion on possible avenues for future research.

Sign in / Sign up

Export Citation Format

Share Document