Physical Sciences and Mathematics Commons^™

Sentiment Analysis Of Public Perception Towards Elon Musk On Reddit (2008-2022), Daniel Maya Bonilla, Samuel Iradukunda, Pamela Thomas

The Cardinal Edge

As Elon Musk’s influence in technology and business continues to expand, it becomes crucial to comprehend public sentiment surrounding him in order to gauge the impact of his actions and statements. In this study, we conducted a comprehensive analysis of comments from various subreddits discussing Elon Musk over a 14-year period, from 2008 to 2022. Utilizing advanced sentiment analysis models and natural language processing techniques, we examined patterns and shifts in public sentiment towards Musk, identifying correlations with key events in his life and career. Our findings reveal that public sentiment is shaped by a multitude of factors, including his …

Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin

Research Collection School Of Computing and Information Systems

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment …

Detecting Toxicity Triggers In Online Discussions, Hamad Bin Khalifa University, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Despite the considerable interest in the detection of toxic comments, there has been little research investigating the causes -- i.e., triggers -- of toxicity. In this work, we first propose a formal definition of triggers of toxicity in online communities. We proceed to build an LSTM neural network model using textual features of comments, and then, based on a comprehensive review of previous literature, we incorporate topical and sentiment shift in interactions as features. Our model achieves an average accuracy of 82.5% of detecting toxicity triggers from diverse Reddit communities.

The Challenges Of Creating Engaging Content: Results From A Focus Group Study Of A Popular News Media Organization, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

The process of content creation for distribution via social media platforms is not a trivial one for social media editors as the goal of creating both serious and engaging content is challenging, with no clear or differing guidelines or rules across and between platforms. For creators of serious content, such as news organizations, advertisers, or educational institutions, engagement has a deeper meaning beyond likes, shares, etc. that is aimed at the audience actually processing the underlying content associated with a social media post. In this research, we report findings from a group study that aimed to understand the process and …

The Global Disinformation Order: 2019 Global Inventory Of Organised Social Media Manipulation, Samantha Bradshaw, Philip N. Howard

Copyright, Fair Use, Scholarly Communication, etc.

Executive Summary

Over the past three years, we have monitored the global organization of social media manipulation by governments and political parties. Our 2019 report analyses the trends of computational propaganda and the evolving tools, capacities, strategies, and resources.

1. Evidence of organized social media manipulation campaigns which have taken place in 70 countries, up from 48 countries in 2018 and 28 countries in 2017. In each country, there is at least one political party or government agency using social media to shape public attitudes domestically.

2.Social media has become co-opted by many authoritarian regimes. In 26 countries, computational propaganda …

Aidr: Artificial Intelligence For Disaster Response, Muhammad Imran, Carlos Castillo, Ji Lucas, Patrick Meier, Sarah Vieweg

Muhammad Imran

We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people post during disasters into a set of user-defined categories of information (e.g., "needs", "damage", etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during …