scholarly journals Political Attacks in 280 Characters or Less: A New Tool for the Automated Classification of Campaign Negativity on Social Media

2021 ◽  
pp. 1532673X2110556
Author(s):  
Vladislav Petkevic ◽  
Alessandro Nai
Keyword(s):  
Social Media ◽  
Convergent Validity ◽  
The Novel ◽  
Independent Data ◽  
Automated Classification ◽  
Automated Coding ◽  
Senate Election ◽  
Automated Classifier ◽  
Relevant Factors

Negativity in election campaign matters. To what extent can the content of social media posts provide a reliable indicator of candidates' campaign negativity? We introduce and critically assess an automated classification procedure that we trained to annotate more than 16,000 tweets of candidates competing in the 2018 Senate Midterms. The algorithm is able to identify the presence of political attacks (both in general, and specifically for character and policy attacks) and incivility. Due to the novel nature of the instrument, the article discusses the external and convergent validity of these measures. Results suggest that automated classifications are able to provide reliable measurements of campaign negativity. Triangulations with independent data show that our automatic classification is strongly associated with the experts’ perceptions of the candidates’ campaign. Furthermore, variations in our measures of negativity can be explained by theoretically relevant factors at the candidate and context levels (e.g., incumbency status and candidate gender); theoretically meaningful trends are also found when replicating the analysis using tweets for the 2020 Senate election, coded using the automated classifier developed for 2018. The implications of such results for the automated coding of campaign negativity in social media are discussed.

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 Automated Faint Galaxy Counts and Galactic Evolution

1980 ◽  
Vol 92 ◽  
pp. 1-8
Author(s):  
J. Anthony Tyson ◽  
John F. Jarvis
Keyword(s):  
Galaxy Evolution ◽  
Automated Detection ◽  
Galactic Evolution ◽  
Automated Classification ◽  
Faint Object ◽  
Statistical Studies ◽  
Automated Classifier ◽  
Analysis System ◽  
Number Counts

Detection and classification of faint images by eye has traditionally encountered systematic errors faintwards of 20th mag on Schmidt plates and 22nd mag on 4-meter plates. Automated classification of Schmidt plate images has pushed the classification limit to 22 mag (Kibblewhite, et al., 1975). Automated detection and classification of faint 4-meter limit plate images has recently led to statistical studies of galaxy numbers and clustering at redshifts where cosmology and galactic evolution dominate over local effects. Here we report on some aspects of the FOCAS (Faint Object Classification and Analysis System) automated classifier (Tyson and Jarvis, 1979) and compare our results of number counts in SA57 with those of Kron, 1979. Differential galaxy counts in six high latitude fields and evidence for galaxy evolution are briefly discussed.

scholarly journals Automated Classification of Evidence of Respect in the Communication through Twitter

2021 ◽  
Vol 11 (3) ◽  
pp. 1294
Author(s):  
Krzysztof Fiok ◽  
Waldemar Karwowski ◽  
Edgar Gutierrez ◽  
Tameika Liciaga ◽  
Alessandro Belmonte ◽  
...  
Keyword(s):  
Social Media ◽  
Text Analysis ◽  
Hate Speech ◽  
Automatic Detection ◽  
Data Sets ◽  
Automated Classification ◽  
Data Set ◽  
Analysis Methods ◽  
Textual Data

Volcanoes of hate and disrespect erupt in societies often not without fatal consequences. To address this negative phenomenon scientists struggled to understand and analyze its roots and language expressions described as hate speech. As a result, it is now possible to automatically detect and counter hate speech in textual data spreading rapidly, for example, in social media. However, recently another approach to tackling the roots of disrespect was proposed, it is based on the concept of promoting positive behavior instead of only penalizing hate and disrespect. In our study, we followed this approach and discovered that it is hard to find any textual data sets or studies discussing automatic detection regarding respectful behaviors and their textual expressions. Therefore, we decided to contribute probably one of the first human-annotated data sets which allows for supervised training of text analysis methods for automatic detection of respectful messages. By choosing a data set of tweets which already possessed sentiment annotations we were also able to discuss the correlation of sentiment and respect. Finally, we provide a comparison of recent machine and deep learning text analysis methods and their performance which allowed us to demonstrate that automatic detection of respectful messages in social media is feasible.

scholarly journals Fake News Detection Using Machine Learning Ensemble Methods

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Iftikhar Ahmad ◽  
Muhammad Yousaf ◽  
Suhail Yousaf ◽  
Muhammad Ovais Ahmad
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Information Dissemination ◽  
Ensemble Methods ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Automated Classification ◽  
Social Media Platforms ◽  
Real World Datasets

The advent of the World Wide Web and the rapid adoption of social media platforms (such as Facebook and Twitter) paved the way for information dissemination that has never been witnessed in the human history before. With the current usage of social media platforms, consumers are creating and sharing more information than ever before, some of which are misleading with no relevance to reality. Automated classification of a text article as misinformation or disinformation is a challenging task. Even an expert in a particular domain has to explore multiple aspects before giving a verdict on the truthfulness of an article. In this work, we propose to use machine learning ensemble approach for automated classification of news articles. Our study explores different textual properties that can be used to distinguish fake contents from real. By using those properties, we train a combination of different machine learning algorithms using various ensemble methods and evaluate their performance on 4 real world datasets. Experimental evaluation confirms the superior performance of our proposed ensemble learner approach in comparison to individual learners.

DETECTION DESTRUCTIVE CONTENT IN SOCIAL MEDIA BASED ON MACHINE LEARNING MODELS

2021 ◽  
pp. 7-20
Author(s):  
Владимир Александрович Минаев ◽  
Алена Дмитриевна Реброва ◽  
Александр Викторович Симонов
Keyword(s):  
Social Media ◽  
System Integration ◽  
Personal Data ◽  
Principal Component ◽  
Feature Space ◽  
User Profile ◽  
Bag Of Words ◽  
Automated Classification ◽  
Learning Models

В статье обсуждаются модели классификации текстового контента и методы его предварительной обработки с целью выявления деструктивных воздействий в социальных медиа. Показано, что основным источником деструктивного контента выступает профиль пользователей, характеризующийся набором личным данных, содержанием публикаций, параметрами сообщества, аккаунтов сети, сообщений и чатов. Говорится об актуальности автоматизированного сбора и анализа данных с помощью моделей прецедентного и дедуктивного обучения. Рассматриваются их основные разновидности и задачи, решаемые на их основе, включающие прогнозирование и типологизацию в аспекте деструктивного содержания текстов, снижение размерности признаков их описания. Исследованы и применены основные методы векторизации текстов: Bag of Words, TF_IDF, Word2vec. На практических корпусах текстов из социальной сети ВКонтакте решены задачи выявления деструктивного контента, связанного с радикальным исламом. Показано, что с помощью примененных моделей и методов все тексты, включающие деструктивный контент, классифицированы верно. Наиболее высокую точность (0,97) при решении задачи распознавания деструктивного контента дает системная интеграция алгоритма векторизации Bag of Words, метода главных компонент для снижения пространства признаков описания текстов и логистической регрессии или случайного леса как моделей обучения. Сделан вывод, что наборы данных, имеющие связь с исламским радикализмом, характеризуются достаточно четкими признаками, которые хорошо вычисляемы с помощью современных моделей, методов и алгоритмов, и могут эффективно применяться для автоматизированной классификации текстовых массивов с целью выявления их деструктивной направленности. Развитие направления, представленного в статье, связано с увеличением исследуемых корпусов документов, более детальным анализом текстов на основе сложных моделей распознавания латентной экстремистской пропаганды, в том числе - представленной в фото, аудио- и видеоформатах. The article discusses models of classification of text content and methods of its pre-processing in order to identify destructive influences in social media. It is shown that the main source of destructive content is the user profile, which is characterized by a set of personal data, the content of publications, community parameters, network accounts, messages and chats. Automated data collection and analysis using case-based and deductive learning models is discussed. We consider their main varieties and the tasks solved on their basis, including forecasting and typology in the aspect of the destructive content of texts, reducing the dimension of the features of their description. The main methods of text vectorization are investigated and applied: Bag of Words, TF_IDF, Word2vec. The tasks of identifying destructive content related to Islamic radicalism are solved on the practical corpus of texts from the social network VKontakte. It is shown that using the applied models and methods, all texts that include destructive content are classified correctly. The highest accuracy (0.97) in solving the problem of recognizing destructive content is provided by the system integration of the Bag of Words vectorization algorithm, the principal component method for reducing the feature space of text descriptions, and logistic regression or random forest as learning models. It is concluded that the data sets associated with Islamic radicalism are characterized by sufficiently clear features that are well calculated using modern models, methods and algorithms, and can be effectively used for automated classification of text arrays in order to identify their destructive orientation. The development of the direction presented in the article is associated with an increase in the studied corpus of documents, a more detailed analysis of texts based on complex models for recognizing latent extremist propaganda, including those presented in photo, audio and video formats.

Values in Action Inventory of Strengths (VIA-IS): Translation and Validation in Urdu Language

2020 ◽  
Vol 35 (1) ◽  
pp. 163-189
Author(s):  
Afifa Anjum ◽  
Naumana Amjad
Keyword(s):  
Factor Structure ◽  
Convergent Validity ◽  
Character Strengths ◽  
Future Studies ◽  
Structure Factors ◽  
Adult Participants ◽  
Positive Correlations ◽  
Cross Language ◽  
Translation And Validation

Values in Action is a classification of 24 character strengths grouped under six virtue categories. This classification is claimed to be universal across cultures and religions (Peterson & Seligman, 2004) and its measure that is, Values in Action Inventory of Strengths (VIA-IS) has been translated and validated in many languages. The present study aimed at its Urdu translation and validation on Pakistani adults taken from different educational institutes and workplaces. Study comprised two parts. Part I dealt with the translation and cross-language validation while in Part II, Construct validation on a sample of 542 adults and convergent validity on a sample of 210 adult participants were determined. Findings revealed satisfactory alpha coefficients for Urdu version. Significant positive correlations with positive affect and life satisfaction and negative correlations with negative affect were indicators of its convergent validity. Age was negatively associated with five strengths whereas significant gender differences were found on seven strengths. Social desirability effects were nonsignificant. Strength-to-virtue level factor structure exploration resulted in a theoretically meaningful four factor structure. Factors were named as Interpersonal, Cognitive, Vitality, and Transcendence and were comparable to factor structures proposed in studies on VIA-IS from a few other cultures. The study offers a valid Urdu translation for use in future studies with adult Urdu speaking population.

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