Social media has become a platform for information diffusion, voicing concerns of existing inequalities and raising public awareness of various social and societal issues. Despite the social good, social media has become a fertile ground for spreading misinformation, hate speech and conspiracy theories. The death of George Floyd in May 2020 triggered a series of protests worldwide in support of the Black Lives Matter (BLM) movement and triggered a debate about equity, inclusion and social justice. The purpose of this study is to examine the impact of misinformation and social noise on the original intended message of BLM using data from the Twitter hashtag ‘BLM’. Results from topic modelling have shown the strong presence of misinformation and social noise. Such information was most probably intended to influence, mislead and dilute the original intended message. However, despite the effort to distort the original message of BLM, results from sentiment analysis show that users’ opinions of the BLM movement remained positive.

There is still a great deal of vaccination hesitancy among different populations in the U.S. including health workers, government officials, minorities and marginalized communities. The politicization of the COVID-19 issue made it difficult to contain the infection and led to an increase in the spread of COVID-19 misinformation and conspiracy theories on the internet and social media. Misinformation about COVID-19 and its vaccine hindered efforts to fight the disease and continue to impact measures to contain the pandemic. In this paper, we examine the factors impacting vaccine hesitancy and acceptance in the United States. Data collected from CDC website and Twitter hashtags, #COVIDvaccines was analyzed, and the results have shown that vaccine hesitancy is largely influenced by personal opinion rather than scientific knowledge. Safety and side effect was the major impacting factor followed by misinformation and conspiracy theories. ALISE RESEARCH TAXONOMY TOPICS big data; data visualization; information literacy; information use; social media. AUTHOR KEYWORDS COVID-19 vaccine; misinformation; conspiracy theory; fake news; vaccine hesitancy.

Social noise and social entropy are two concepts that have been the subject of studies in the last two decades (Bruno, 2010; Matei et al.,2010; Alsaid et al., 2020; Alsaid & Pampapura, 2022; Pampapura et al., 2022). Studies that discussed misinformation, fake news, conspiracy theories, controversies, and disinformation also focused on certain aspects of social noise (Romer & Jamieson, 2020; Van Prooijen & Douglas, 2018). In this study, social noise refers to users intentionally or unintentionally participating in creating, disseminating, or spreading misinformation on social media. Recent studies have identified six characteristics or constructs of social noise. The six constructs identified by the recent studies include image curation, relationship management, cultural agency, conflict engagement, affiliation & politics, and norms & beliefs. In this study, we use the constructs to quantify and measure social noise using entropy and uncertainty reduction theory. The study focuses on two questions. How do we measure entropy as a degree of uncertainty caused by social noise? How does reducing uncertainty and social noise contribute to reducing the spread of misinformation? The primary analysis indicated that entropy as a measure of disorders of a tweet 1.5. This result is in line with the results produced by Son et al. (2019). Sentiment analysis results show that 66.6% of the tweets are subjective, while 33.4 % of the tweets are objective (facts). Topic modeling results also indicated the presence of keywords that is characteristics of social noise constructs in the dataset.

Social noise and social entropy are new concepts modeled after Shannon’s information and communication theory, in which the interference of noise between the sender and receiver is measured using entropy. Social noise in the context of social media plays a vital role in magnifying and spreading misinformation, which in turn impacts the overall information ecosystem. Ecosystems are made of interconnected and integrated parts that rely on one another to maintain balance and survive. Studies related to social noise and misinformation have shown that social noise can contribute significantly to spreading misinformation and potentially alter the original intended message (Alsaid & Pampapura, 2022); Alsaid et al. (2024). paper investigates methods of quantifying social noise using entropy to minimize the spread of misinformation on social media, particularly X. Using a combination of Uncertainty Reduction Theory (URT) and Social Entropy, data analysis was performed using one million tweets harvested from #Ukraine. Data analysis involved several methods: sentiment analyses, term association, network maps, and entropy computation. Results have shown a direct relationship between social noise and social entropy as a measure of uncertainty. Also, social noise and uncertainty decrease with the use of URLs and rich content. It is evident from the results that the entropy value is influenced by the accuracy of keywords identified using topic modeling as descriptive of social noise constructs. Semantic analysis of tweets can help improve the definition of social noise constructs, leading to enhanced and more accurate entropy calculation. Future studies may consider advanced machine learning and AI