scholarly journals Identification of Fake vs. Real Identities on Social Media using Random Forest and Deep Convolutional Neural Network

2019 ◽  
Vol 9 (1) ◽  
pp. 7347-7351
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Random Forest ◽  
Convolutional Neural Network ◽  
Recurrent Neural Network ◽  
Deep Convolutional Neural Network ◽  
Social Media Platforms

Identity detection is very essential in social media platforms, various platform has facing fake accounts influence since couple of years in current eras. Many researchers has introduces approach for identify the fake profiles, but still system cant able to solve such issues. As these fake identities are being used by offenders for various malicious purposes, it has become necessity of time to identify them. The fake identities are categorized into two main types’ i.e. fake identities by bots and fake identities by humans. This system removes fake identities by bots during preprocessing and focuses mainly on identification of fake identities by humans as very little research has been made till now on the fake identities by humans. For classification we test for two different algorithms i.e. Random Forest (RF) and Recurrent Neural Network (RNN). The classification is based on various features such as user name, location, friends count, followers count and so on. Here, dataset used is that of Twitter.

scholarly journals Deep Convolutional Neural Network for Mapping Smallholder Agriculture Using High Spatial Resolution Satellite Image

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2398 ◽  
Author(s):  
Bin Xie ◽  
Hankui K. Zhang ◽  
Jie Xue
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Spatial Information ◽  
Deep Convolutional Neural Network ◽  
Crop Types ◽  
Pooling Strategy

In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically explore high level spatial information and thus is promising for such tasks. This study tried to evaluate different CNN structures for classification of four smallholder agricultural landscapes in Heilongjiang, China using pan-sharpened 2 m GaoFen-1 (meaning high resolution in Chinese) satellite images. CNN with three pooling strategies: without pooling, with max pooling and with average pooling, were evaluated and compared with random forest. Two different numbers (~70,000 and ~290,000) of CNN learnable parameters were examined for each pooling strategy. The training and testing samples were systematically sampled from reference land cover maps to ensure sample distribution proportional to the reference land cover occurrence and included 60,000–400,000 pixels to ensure effective training. Testing sample classification results in the four study areas showed that the best pooling strategy was the average pooling CNN and that the CNN significantly outperformed random forest (2.4–3.3% higher overall accuracy and 0.05–0.24 higher kappa coefficient). Visual examination of CNN classification maps showed that CNN can discriminate better the spectrally similar crop types by effectively exploring spatial information. CNN was still significantly outperformed random forest using training samples that were evenly distributed among classes. Furthermore, future research to improve CNN performance was discussed.

scholarly journals CNN based efficient image classification system for smartphone device

2021 ◽  
Author(s):  
Mayank Mishra ◽  
Tanupriya Choudhury ◽  
Tanmay Sarkar
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Classification System ◽  
Text Messaging ◽  
Motivational Messages ◽  
Social Media Text ◽  
Social Media Platforms ◽  
Optimum Model

Abstract In our work, we look to classify images that make their way into our smartphone devices through various social-media text-messaging platforms. We aim at classifying images into three broad categories: document-based images, quote-based images, and photographs. People, especially students, share many document-based images that include snapshots of essential emails, handwritten notes, articles, etc. Quote based images, consisting of birthday wishes, motivational messages, festival greetings, etc., are among the highly shared images on social media platforms. A significant share of images constitutes photographs of people, including group photographs, selfies, portraits, etc. We train various convolutional neural network (CNN) based models on our self-made dataset and compare their results to find our task’s optimum model.

scholarly journals Using Machine Learning–Based Approaches for the Detection and Classification of Human Papillomavirus Vaccine Misinformation: Infodemiology Study of Reddit Discussions

10.2196/26478 ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. e26478
Author(s):  
Jingcheng Du ◽  
Sharice Preston ◽  
Hanxiao Sun ◽  
Ross Shegog ◽  
Rachel Cunningham ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Social Media ◽  
Human Papillomavirus ◽  
Convolutional Neural Network ◽  
Hpv Vaccine ◽  
Support Vector ◽  
Social Media Platforms ◽  
Vaccine Promotion

Background The rapid growth of social media as an information channel has made it possible to quickly spread inaccurate or false vaccine information, thus creating obstacles for vaccine promotion. Objective The aim of this study is to develop and evaluate an intelligent automated protocol for identifying and classifying human papillomavirus (HPV) vaccine misinformation on social media using machine learning (ML)–based methods. Methods Reddit posts (from 2007 to 2017, N=28,121) that contained keywords related to HPV vaccination were compiled. A random subset (2200/28,121, 7.82%) was manually labeled for misinformation and served as the gold standard corpus for evaluation. A total of 5 ML-based algorithms, including a support vector machine, logistic regression, extremely randomized trees, a convolutional neural network, and a recurrent neural network designed to identify vaccine misinformation, were evaluated for identification performance. Topic modeling was applied to identify the major categories associated with HPV vaccine misinformation. Results A convolutional neural network model achieved the highest area under the receiver operating characteristic curve of 0.7943. Of the 28,121 Reddit posts, 7207 (25.63%) were classified as vaccine misinformation, with discussions about general safety issues identified as the leading type of misinformed posts (2666/7207, 36.99%). Conclusions ML-based approaches are effective in the identification and classification of HPV vaccine misinformation on Reddit and may be generalizable to other social media platforms. ML-based methods may provide the capacity and utility to meet the challenge involved in intelligent automated monitoring and classification of public health misinformation on social media platforms. The timely identification of vaccine misinformation on the internet is the first step in misinformation correction and vaccine promotion.

scholarly journals Emotional Analysis of Bogus Statistics in Social Media

2020 ◽  
Vol 2 (3) ◽  
pp. 178-186
Author(s):  
Dr. Wang Haoxiang
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Recurrent Neural Network ◽  
Research Work ◽  
Vital Role ◽  
Information Analysis ◽  
The Social ◽  
The World ◽  
Social Media Platforms ◽  
The Impact

Fake info or bogus statistics is a new term and it is now considered as a greatest threat to democracy. Since the world is full of surprises and humans have developed their delicate nature to detect unexcepted information. Social media plays a vital role in information spreading, since the impact towards fake information has gained more attention due to the social media platforms. Trending the hot topic without analyzing the information will introduce great impact over millions of people. So, it is essential to analyze the message and its truthfulness. Emotional analysis is an important factor in bogus statistics as the information gets reshared among other based on individual emotions. Considering these facts in social media information analysis, an efficient emotional analysis for bogus statistics in social media is proposed in this research work using recurrent neural network. In an emotional perspective, fake messages are compared with actual message and false messages are identified experimentally using recurrent neural network.

scholarly journals A new corpus-based convolutional neural network for big data text analytics

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Wedjdane Nahilia ◽  
Kahled Rezega ◽  
Okba Kazara
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Large Scale ◽  
Easy Access ◽  
Data Sets ◽  
Accuracy Score ◽  
Social Media Platforms

Companies market their services and products on social media platforms with today's easy access to the internet. As result, they receive feedback and reviews from their users directly on their social media sites. Reading every text is time-consuming and resourcedemanding. With access to technology-based solutions, analyzing the sentiment of all these texts gives companies an overview of how positive or negative users are on specific subjects will minimize losses. In this paper, we propose a deep learning approach to perform sentiment analysis on reviews using a convolutional neural network model, because that they have proven remarkable results for text classification. We validate our convolutional neural network model using large-scale data sets: IMDB movie reviews and Reuters data sets with a final accuracy score of ~86% for both data sets.

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