scholarly journals Automated Classification of Fake News Spreaders to Break the Misinformation Chain

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 248
Author(s):  
Simone Leonardi ◽  
Giuseppe Rizzo ◽  
Maurizio Morisio
Keyword(s):  
Social Media ◽  
Network Architecture ◽  
Diffusion Mechanism ◽  
Automated Classification ◽  
Fake News ◽  
Neural Network Architecture ◽  
User Classification ◽  
The Social ◽  
Network Topologies

In social media, users are spreading misinformation easily and without fact checking. In principle, they do not have a malicious intent, but their sharing leads to a socially dangerous diffusion mechanism. The motivations behind this behavior have been linked to a wide variety of social and personal outcomes, but these users are not easily identified. The existing solutions show how the analysis of linguistic signals in social media posts combined with the exploration of network topologies are effective in this field. These applications have some limitations such as focusing solely on the fake news shared and not understanding the typology of the user spreading them. In this paper, we propose a computational approach to extract features from the social media posts of these users to recognize who is a fake news spreader for a given topic. Thanks to the CoAID dataset, we start the analysis with 300 K users engaged on an online micro-blogging platform; then, we enriched the dataset by extending it to a collection of more than 1 M share actions and their associated posts on the platform. The proposed approach processes a batch of Twitter posts authored by users of the CoAID dataset and turns them into a high-dimensional matrix of features, which are then exploited by a deep neural network architecture based on transformers to perform user classification. We prove the effectiveness of our work by comparing the precision, recall, and f1 score of our model with different configurations and with a baseline classifier. We obtained an f1 score of 0.8076, obtaining an improvement from the state-of-the-art by 4%.

scholarly journals Overcoming Language Variation in Sentiment Analysis with Social Attention

2017 ◽  
Vol 5 ◽  
pp. 295-307 ◽  
Author(s):  
Yi Yang ◽  
Jacob Eisenstein
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Social Media ◽  
Social Network ◽  
Sentiment Analysis ◽  
Network Architecture ◽  
Language Variation ◽  
Neural Network Architecture ◽  
The Social ◽  
Classification Function

Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author’s position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and on review data.

scholarly journals Sentiment Analysis in Myanmar Language using Convolutional LSTM Neural Network

2021 ◽  
Vol 10 (04) ◽  
pp. 15-19
Author(s):  
Nwet Yin Tun Thein ◽  
Khin Mar Soe
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Sentiment Analysis ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
The People ◽  
Sentiment Lexicon ◽  
Use Of Social Media ◽  
Convolutional Lstm

In recent years, there has been an increasing use of social media among people in Myanmar and writing review on social media pages about the product, movie, and trip are also popular among people. Moreover, most of the people are going to find the review pages about the product they want to buy before deciding whether they should buy it or not. Extracting and receiving useful reviews over interesting products is very important and time consuming for people. Sentiment analysis is one of the important processes for extracting useful reviews of the products. In this paper, the Convolutional LSTM neural network architecture is proposed to analyse the sentiment classification of cosmetic reviews written in Myanmar Language. The paper also intends to build the cosmetic reviews dataset for deep learning and sentiment lexicon in Myanmar Language.

scholarly journals Linguistic drivers of misinformation diffusion on social media during the COVID-19 pandemic

2021 ◽  
Author(s):  
Giandomenico Di Domenico ◽  
Annamaria Tuan ◽  
Marco Visentin
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
New Technologies ◽  
Fake News ◽  
Conspiracy Theories ◽  
Social Media Platform ◽  
The Social ◽  
Media Platform ◽  
Textual Cues ◽  
Twitter Users

AbstractIn the wake of the COVID-19 pandemic, unprecedent amounts of fake news and hoax spread on social media. In particular, conspiracy theories argued on the effect of specific new technologies like 5G and misinformation tarnished the reputation of brands like Huawei. Language plays a crucial role in understanding the motivational determinants of social media users in sharing misinformation, as people extract meaning from information based on their discursive resources and their skillset. In this paper, we analyze textual and non-textual cues from a panel of 4923 tweets containing the hashtags #5G and #Huawei during the first week of May 2020, when several countries were still adopting lockdown measures, to determine whether or not a tweet is retweeted and, if so, how much it is retweeted. Overall, through traditional logistic regression and machine learning, we found different effects of the textual and non-textual cues on the retweeting of a tweet and on its ability to accumulate retweets. In particular, the presence of misinformation plays an interesting role in spreading the tweet on the network. More importantly, the relative influence of the cues suggests that Twitter users actually read a tweet but not necessarily they understand or critically evaluate it before deciding to share it on the social media platform.

Polarity Classification of Arabic Sentiments

2016 ◽  
Vol 11 (3) ◽  
pp. 32-49 ◽  
Author(s):  
Mohammed N. Al-Kabi ◽  
Heider A. Wahsheh ◽  
Izzat M. Alsmadi
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Operating Characteristic ◽  
Opinion Mining ◽  
Online Social Network ◽  
The Social ◽  
Polarity Classification ◽  
Arabic Sentiment Analysis ◽  
Modern Standard

Sentiment Analysis/Opinion Mining is associated with social media and usually aims to automatically identify the polarities of different points of views of the users of the social media about different aspects of life. The polarity of a sentiment reflects the point view of its author about a certain issue. This study aims to present a new method to identify the polarity of Arabic reviews and comments whether they are written in Modern Standard Arabic (MSA), or one of the Arabic Dialects, and/or include Emoticons. The proposed method is called Detection of Arabic Sentiment Analysis Polarity (DASAP). A modest dataset of Arabic comments, posts, and reviews is collected from Online social network websites (i.e. Facebook, Blogs, YouTube, and Twitter). This dataset is used to evaluate the effectiveness of the proposed method (DASAP). Receiver Operating Characteristic (ROC) prediction quality measurements are used to evaluate the effectiveness of DASAP based on the collected dataset.

Social Media Handbuch

2021 ◽  
Keyword(s):  
Social Media ◽  
Data Processing ◽  
Business Models ◽  
Practical Application ◽  
Strategy Model ◽  
The Social ◽  
Platform Economy

The Social Media Handbook provides guidance on long-term developments in the ever-changing social media sector and explains fundamental interrelationships in this field. It describes a strategy model for the development of one’s own solutions, summarises the theories, methods and models of leading authors and shows their practical application, while also highlighting current developments and dealing with the topic of data processing in social media. An examination of the platform economy with its economic functions facilitates the classification of business models in social media. The book also shows how platforms and their algorithms can influence our actions and shape our opinions. With contributions by Prof. Karin Bjerregaard Schlüter, Andrea Braun, Franziska Geue, Tobias Knopf, Markus Korbien, Prof. Dr. Daniel Michelis, Stefan Pfaff, Thanh H. Pham, Tom Reichstein, Prof. Dr. Anna Riedel, Michael Sarbacher, Prof. Dr. Dr. Thomas Schildhauer, Prof. Dr. Hendrik Send, Dr. Stefan Stumpp, Prof. Dr. Sebastian Volkmann, Jan-Benedikt Weber, Julia Weißhaupt, Norman Wiebach und Prof. Dr. Christian Wissing.

scholarly journals Native Advertising—The Formats and Types of Content Most Demanded by Brands in the Spanish Media

2021 ◽  
Vol 2 (4) ◽  
pp. 529-544
Author(s):  
Daniel Zomeño ◽  
Rocío Blay-Arráez
Keyword(s):  
Social Media ◽  
Rapid Development ◽  
Editorial Content ◽  
Media Environment ◽  
The Social ◽  
Media Ecosystem ◽  
The Media ◽  
Media Formats ◽  
Depth Interviews

Media convergence and the incorporation of new narratives typical of the consumption habits of younger audiences in the social media environment have led to the proliferation of a wide variety of formats and types of content in the media ecosystem through which the editorial content offered to brands is being distributed. This qualitative research, using in-depth interviews with a qualified sample of branded content managers from the main Spanish media, allows us to determine the main characteristics of the native advertising demanded by advertisers. The results corroborate observations that content channelled through more sophisticated consumption experiences, using both multimedia and interactivity with a clear transmedia approach, tends to be better received by the audience and, therefore, in greater demand by brands. It also confirms that both video and social media formats have grown exponentially when it comes to providing an outlet for branded content. Based on the results obtained, a proposed classification of these products, including definitions, has been drawn up so they can be publicised to the professional world, offering the reflection and precision that their rapid development has not allowed until now.

scholarly journals Loodus tuleb tagasi: delfiinid ja saurused. Libauudistest, photoshop-muistenditest ja meemidest koroonakarantiini ajal

Mäetagused ◽  
2021 ◽  
Vol 79 ◽  
pp. 167-184
Author(s):  
Eda Kalmre ◽  
Keyword(s):  
Social Media ◽  
Coping Strategies ◽  
Well Being ◽  
Point Of View ◽  
Human Existence ◽  
The Other ◽  
Fake News ◽  
The Social ◽  
Truth Value ◽  
Positive Effect

The article follows the narrative trend initiated by the social media posts and fake news during the first months of the corona quarantine, which claims that the decrease of contamination due to the quarantine has a positive effect on the environment and nature recovery. The author describes the context of the topic and follows the changes in the rhetoric through different genres, discussing the ways in which a picture can tell a truthful story. What is the relation between the context, truth, and rhetoric? This material spread globally, yet it was also readily “translated” into the Estonian context, and – what is very characteristic of the entire pandemic material – when approaching this material, truthful and fabricated texts, photos, and videos were combined. From the folkloristic point of view, these rumours in the form of fake news, first presented in the function of a tall tale and further following the sliding truth scale of legends, constitute a part of coping strategies, so-called crisis humour, yet, on the other hand, also a belief story presenting positive imagery, which surrounds the mainly apocalyptically perceived pandemic period and interprets the human existence on a wider scale. Even if these fake news and memes have no truth value, they communicate an idea – nature recovers – and definitely offer hope and a feeling of well-being.

scholarly journals USE OF GENETIC ALGORITHM IN DEEP NEURAL NETWORKS CONFIGURATION FOR THE PURPOSES OF COMPUTER ATTACKS CLASSIFICATION

2020 ◽  
pp. 104-117
Author(s):  
O.S. Amosov ◽  
◽  
S.G. Amosova ◽  
D.S. Magola ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Network Architecture ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Classification Problem ◽  
Neural Network Architecture ◽  
Computer Attacks ◽  
Neural Network Technology

The task of multiclass network classification of computer attacks is given. The applicability of deep neural network technology in problem solving has been considered. Deep neural network architecture was chosen based on the strategy of combining a set of convolution and recurrence LSTM layers. Op-timization of neural network parameters based on genetic algorithm is proposed. The presented results of modeling show the possibility of solving the network classification problem in real time.

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