Annotated bibliography assignment

1.      Islam, M. S., Sarkar, T., Khan, S. H., Kamal, A. H. M., Hasan, S. M., Kabir, A., ... &
Seale, H. (2020). COVID-19–related infodemic and its impact on public health: A global social media
analysis. The American journal of tropical medicine and hygiene, 103(4), 1621.


In their article "COVID-19–related infodemic and its impact on public health: A global social media
analysis," Islam et al. conducted a global social media analysis to examine the impact of the
COVID-19 infodemic on public health. The authors found that the infodemic on social media had a
significant impact on public health, with a substantial portion of information being misleading and
causing confusion among people. Conspiracy theories, stigmatization, and racism were among the most
prevalent themes identified. The authors emphasized the need for effective communication strategies
by public health authorities to address the misinformation and to prevent the spread of rumors.
This article provides valuable insights for policymakers and public health authorities on the
prevalence of misinformation and the themes that were commonly shared on social media platforms. It
highlights the importance of combating the infodemic and ensuring accurate information reaches the
public.


2.      Ghani, N. A., Hamid, S., Hashem, I. A. T., & Ahmed, E. (2019). Social media big data
analytics: A survey. Computers in Human Behavior, 101, 417-428.


The article "Social media big data analytics: A survey" by Ghani et al. presents a comprehensive
survey of social media big data analytics. The authors discuss the evolution of social media and
the emergence of big data analytics, highlighting the benefits of social media big data analytics
in various fields such as marketing, healthcare, and politics. They discuss the challenges
associated with social media big data analytics, including the volume and complexity of data,
privacy and security concerns, and the need for advanced analytical tools and techniques. The
authors also provide an overview of the state-of-the-art technologies and methods used for social
media big data analytics, including sentiment analysis, network analysis, and machine learning. The
article serves as a valuable resource for researchers and practitioners interested in social media
big data analytics, providing an overview of the current state of the field, the challenges, and
the potential benefits of utilizing social media data. Overall, the article provides a
comprehensive overview of social media big data analytics, highlighting the importance of utilizing
these analytics in various fields for informed decision-making.


3.      Chang, Y. C., Ku, C. H., & Chen, C. H. (2019). Social media analytics: Extracting and
visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information
Management, 48, 263-279.


In their article "Social media analytics: Extracting and visualizing Hilton hotel ratings and
reviews from TripAdvisor," Chang et al. explore the use of social media analytics in extracting and
visualizing hotel ratings and reviews from TripAdvisor. The authors used a web scraping technique
to collect Hilton hotel ratings and reviews from TripAdvisor and analyzed the data using sentiment
analysis and topic modeling techniques. The study found that sentiment analysis could be used to
identify the sentiment of hotel reviews, while topic modeling could identify the most frequently
mentioned topics in the reviews. The authors also developed a visual dashboard that displayed the
sentiment and topics of the reviews, which could provide valuable insights for hotel managers to
improve their services and customer experience. This article highlights the importance of social
media analytics in extracting and visualizing customer feedback and provides a practical example of
its application in the hotel industry. The authors' approach could be applied to other industries
to gain valuable insights into customer sentiment and feedback.