scholarly journals Abusive language detection in youtube comments leveraging replies as conversational context

2021 ◽  
Vol 7 ◽  
pp. e742
Author(s):  
Noman Ashraf ◽  
Arkaitz Zubiaga ◽  
Alexander Gelbukh
Keyword(s):  
Social Media ◽  
Classification Accuracy ◽  
Contextual Information ◽  
Original Description ◽  
Linguistic Features ◽  
Abusive Behavior ◽  
Conversational Context ◽  
Media Experience ◽  
Language Detection

Nowadays, social media experience an increase in hostility, which leads to many people suffering from online abusive behavior and harassment. We introduce a new publicly available annotated dataset for abusive language detection in short texts. The dataset includes comments from YouTube, along with contextual information: replies, video, video title, and the original description. The comments in the dataset are labeled as abusive or not and are classified by topic: politics, religion, and other. In particular, we discuss our refined annotation guidelines for such classification. We report a number of strong baselines on this dataset for the tasks of abusive language detection and topic classification, using a number of classifiers and text representations. We show that taking into account the conversational context, namely, replies, greatly improves the classification results as compared with using only linguistic features of the comments. We also study how the classification accuracy depends on the topic of the comment.

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals Factors of communication mix on social media and their role in forming customer experience and brand image

2018 ◽  
Vol 13 (3) ◽  
pp. 1108-1118 ◽  
Author(s):  
Radovan Bacik ◽  
Richard Fedorko ◽  
Ludovit Nastisin ◽  
Beata Gavurova
Keyword(s):  
Social Media ◽  
Brand Image ◽  
Customer Experience ◽  
Media Environment ◽  
Building Relationships ◽  
Brand Building ◽  
Brand Reputation ◽  
Marketing Activities ◽  
New Perspective ◽  
Media Experience

Abstract Building a brand is a long-term process and it also applies to the world of social media. It is said that building a good brand reputation takes years, but it can be ruin in a moment. Therefore, it is important to look responsibly at all the aspects that have a role in building a brand on social media. The actual experience with the brand on social media is able to significantly affect brand building. The study focuses on exploring brand-building relationships in the social media environment. We selected a set of factors to predict customer experience with the brand in a social media environment and then we examined the relationship between this customer experience and the perceived brand image. 476 respondents filled out the electronic questionnaire. The study puts the greatest emphasis on respondents aged 20 to 35 years. We used correlation analysis to investigate the relationships in this issue. Out of the seven investigated relationships, up to two cases with medium dependence were confirmed by the strong relevance of relationships. The results support the importance of using social media tools for branding purposes, because these tools are the ones with the greatest ability to influence the people’s perception and attitude. It is also the fastest and one of the most personal ways to communicate with the customer. It happens in real time and it can convey the real emotion if performed right which all together help to trigger the user action. The findings of this study can guide marketing activities for companies to make the return on investment in social media as high as possible. The research offers a new perspective on selected factors and their role in creating social media experience and subsequently a brand image.

Attention Based Context Boosted Cyberbullying Detection in Social Media

2021 ◽  
Vol 2 (4) ◽  
pp. 418-433
Author(s):  
Nabi Rezvani ◽  
Amin Beheshti
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Contextual Information ◽  
Language Model ◽  
Multiple Sources ◽  
Learner Model ◽  
Deep Architecture ◽  
Efficient Machine ◽  
Cyberbullying Detection ◽  
Textual Content

Cyberbullying detection is a rising research topic due to its paramount impact on social media users, especially youngsters and adolescents. While there has been an enormous amount of progress in utilising efficient machine learning and NLP techniques for tackling this task, recent methods have not fully addressed contextualizing the textual content to the highest possible extent. The textual content of social media posts and comments is normally long, noisy and mixed with lots of irrelevant tokens and characters, and therefore utilizing an attention-based approach that can focus on more relevant parts of the text can be quite pertinent. Moreover, social media information is normally multi-modal in nature and may contain various metadata and contextual information that can contribute to enhancing the Cyberbullying prediction system. In this research, we propose a novel machine learning method that, (i) fine tunes a variant of BERT, a deep attention-based language model, which is capable of detecting patterns in long and noisy bodies of text; (ii)~extracts contextual information from multiple sources including metadata information, images and even external knowledge sources and uses these features to complement the learner model; and (iii) efficiently combines textual and contextual features using boosting and a wide-and-deep architecture. We compare our proposed method with state-of-the-art methods and highlight how our approach significantly outperforming the quality of results compared to those methods in most cases.

Hate Speech and Offensive Language Detection from Social Media

2021 ◽  
Author(s):  
Vildan Mercan ◽  
Akhtar Jamil ◽  
Alaa Ali Hameed ◽  
Irfan Ahmed Magsi ◽  
Sibghatullah Bazai ◽  
...  
Keyword(s):  
Social Media ◽  
Hate Speech ◽  
Offensive Language ◽  
Language Detection

Impact of General Social Media Experience on Usefulness for Business in the Workplace

2014 ◽  
pp. 14-32 ◽  
Author(s):  
Donald L. Amoroso ◽  
Tsuneki Mukahi ◽  
Mikako Ogawa
Keyword(s):  
United States ◽  
Social Media ◽  
Strong Predictor ◽  
The United States ◽  
Ease Of Use ◽  
Collective Knowledge ◽  
Use Of Social Media ◽  
Media Experience ◽  
Behavioral Intention To Use ◽  
Media Applications

This chapter looks at the adoption of general social media applications on usefulness for business, comparing the factors that influence adoption at work between Japan and the United States. In Japan, ease of use and usefulness for collective knowledge in general social media are predictors of usefulness for business social media, and in the United States, only usefulness for collective knowledge is a strong predictor of usefulness for business. The authors did not find behavioral intention to use social media in the workplace to be an important factor in predicting the usefulness of social media for business. The value of this research is its ability to understand the use of social media in the workplace to include how the experience of social media impacts on the expectation of usefulness for business and how the impact of ease of use differs from Japanese to the United States because of cultural, technological, and market reasons.

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