BLOG INFORMATION CLASSIFICATION

nformation Classification is the categorization of the huge amount of data in an efficient and useful way. In the current scenario data is growing exponentially due to the rise of internet rich applications. One such source of information is the blogs. Blogs are web logs maintained by their authors that contain information related to a certain topic and also contain authors view about that topic. Micro blogs, on the other hands, are variations of blogs that contain smaller data as compared to blogs. Nevertheless, it also contains rich information. In this project, Twitter, a micro blogging website has been targeted to gather information on certain trending topics. The information is in the form of tweets. A tweet is a post or an update on status on the Twitter website. These tweets are extracted using Twitter Search APIs. This data is then classified into different classes based on its content. Using the classified data, features are extracted from the tweets and suggestions are given to the users based on the trending topics

Climate change sentiment on Twitter: An unsolicited public opinion poll

The consequences of anthropogenic climate change are extensively debated through scientific papers, newspaper articles, and blogs. Newspaper articles may lack accuracy, while the severity of findings in scientific papers may be too opaque for the public to understand. Social media, however, is a forum where individuals of diverse backgrounds can share their thoughts and opinions. As consumption shifts from old media to new, Twitter has become a valuable resource for analyzing current events and headline news. In this research, we analyze tweets containing the word climate collected between September 2008 and July 2014. Through use of a previously developed sentiment measurement tool called the Hedonometer, we determine how collective sentiment varies in response to climate change news, events, and natural disasters. We find that natural disasters, climate bills, and oildrilling can contribute to a decrease in happiness while climate rallies, a book release, and a green ideas contest can contribute to an increase in happiness. Words uncovered by our analysis suggest that responses to climate change news are predominately from climate change activists rather than climate change deniers, indicating that Twitter is a valuable resource for the spread of climate change awareness

An Investigation of Domain-based Social Influence on ChatGPT-Related Twitter Data

Recently, the word ChatGPT has made its way into the vocabulary of various fields that are important to society, including healthcare, education, governance, and robotics. This study examines the various reasons for social media influences using the Social Influence Theory. The study used tweets connected to ChatGPT collected from January 1st, 2023 to March 31st, 2023, totaling 402, 965 tweets. Initially, the key domains of ChatGPT discussions were identified along with the variations in the sentiments over the period. Subsequently drawing the literature related to Social Influence Theory, this study explored the relationship between subjective norm, social identity, user commitment, and features of the tweet content with social influence on the readership of a ChatGPT-related tweet. Subjective norm, identity, sentiment and domain are significant factors for social influence. The user commitment suggests a negative relationship with the social influence. Finally, the theoretical and practical implications are explained

A Twitter narrative of the COVID-19 pandemic in Australia

Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management.Comment: Accepted to ISCRAM 202

Social Media and the COVID-19: South African and Zimbabwean Netizens’ Response to a Pandemic

Since the end of 2019, the world faced a major health crisis in the form of the Coronavirus (COVID-19) pandemic. To mitigate the impact of the pandemic, governments across the globe instituted measures such as restricting local and international travel and in many cases, ordering citizens to stay indoors. Considering the social and economic impact of these restrictions it becomes crucial to investigate internet citizens’ (netizens) perception about the precautionary measures adopted. The study is anchored in the digital public sphere theory, which treats social media applications as virtual platforms where netizens commune to share ideas and debate about issues that affect them. Social media platforms already have critical public views on the current pandemic. However, the majority of this data is unstructured and difficult to interpret. Natural language processing (NLP), on the other hand, makes the task of gathering and analysing vast amounts of textual data feasible. Extracting structured knowledge from natural language, however, comes with unique challenges due to diverse linguistic properties including abbreviation, spelling mistakes, punctuations, stop words and non-standard text. In this work, The Latent Dirichlet Allocation (LDA) algorithm was applied to tweeter data to extract topics discussed by netzens from Zimbabwe and South Africa.  The primary focus of this paper, is to comparatively explore the variety of topics that occupied twitter communities from the two countries. We examine whether or not the national identities that define and differentiate citizens of these countries also exist on Twitter as evident in the emerging topics. Furthermore, this work investigated public opinion by analysing how citizens discuss the issues around the COVID-19 pandemic on social medi

Democracy in the Indonesian Digital Public Sphere: Social Network Analysis of Twitter Users' Responses to the Issue of Nationalism Knowledge Test at the Corruption Eradication Commission (TWK-KPK)

The mobility restriction during the COVID-19 pandemic did not stop the public from expressing their opinions. Since they could not go on demonstrations, they moved democracy to the digital sphere, such as on Twitter. Previous research has shown that Twitter users in Indonesia use the platform to express political views and opinions on governmental issues. The issue of the Nationalism Knowledge Test (TWK) at the Corruption Eradication Commission (KPK) was a trending topic on Twitter for a while. The issue spurred discussions on Twitter when 75 employees did not pass the KPK-TWK on May 2021. The discussion then stopped for a moment before picking up again during the official dismissal of the employees on 30 September 2021. This article focuses on the social network analysis of the public’s responses to this issue on Twitter. Social network data were collected using Drone Emprit from May to October 2021 and analyzed using Gephi to generate graphical representations of the social networks. The results reveal the structure of the movement was centralized and dynamic. Regarding the dissemination of information, the most central was news media and anti-corruption activists’ accounts. These accounts mobilized the community on Twitter to make a critical social movement. This means that the digital sphere can be an evolution of democracy form and activism, especially in the anti-corruption movement

Warning: This Is a Must Read : Participation and Disruption in Social Artifacts and Spaces

As I show in three separate case studies, content, technology, and participant relationships are key components in the design of social artifacts and spaces. One study highlights the invention and evolution of content across multiple spaces. The second shows content used as leverage for authority. The last case study examines the relationship between content and technological interfaces and how disruption may not always be successful. All of these components make up what I refer to as disruption. Disruption describes participant acts that are executed to change existing power-based structures of information sharing. Using the insights gained from this research, I develop the concept of disruption as a component of design that emphasizes the value of participant work and the ability of participants to alter existing structures of information sharing