Facebook Posts Of Amazon Tourism by Kaggle | Ecommerce and Consumer Trends

About this Dataset

Facebook Posts Of Amazon Tourism

Facebook Posts of Amazon Tourism

Analyzing Consumer Engagement and Content Trends

About this dataset

This dataset contains valuable public insights into the activities of small to medium tourism companies in the Amazon rainforest region of Brazil. Our team has extensively classified each post into 8 distinct content categories and also associated vital accompanying information such as textual content, post links, type, and published time - alongside key engagement metrics such as number of shares, comments, likes and emotions. Utilize this versatile set to gain essential knowledge about consumer engagement or explore trends within the Brazilian tourism industry operating within a wild ecological wonderland!

More Datasets

For more datasets, click here.

Featured Notebooks

🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains public data from Facebook posts made by small and medium tourism companies operating in the Amazon rainforest region of Brazil between January and June 2018. This dataset can help you gain insights into consumer engagement and content analysis that can help inform business decisions. It is helpful for marketers, researchers, businesses or anyone who wishes to study the impact of social media on different types of customers.

Here are some tips on how to use this dataset:

Review the column descriptions in order to understand what data has been included - this will provide an overview of how each field can be used when analyzing consumer engagement with the posts.

Filter for different content categories that interest you - knowing a post's category allows you to know what type of conversation or insight people are most interested in discussing or seeing from companies operating in the Amazon region.

Look at how many reactions, shares, likes and comments each post has - examining these metrics will tell you which topics are more (or less) popular among customers associated with these posts so that you can identify areas where more focus should be placed when producing content moving forward.

Compare different fields against each other - looking at the post link name, status type and URL associated with a particular post alongside its associated metrics is important context to understand what drives stronger consumer engagement on certain types of content than others.

Create cross-sectional visualizations between key fields such as type vs number engaged (e g likes + shares + comments). Visualizing this data helps compare correlations better so that we have an easier time understanding how no explicitly related but still relevant variables behave together

By following these tips, one should gain an overall understanding about consumer behaviours concerning small and medium-sized businesses operating within Amazon Rainforest region allowing them make informed marketing decisions for their company’s using this specific subsection channels

Research Ideas

Create a custom segmentation of tourism companies in the region based on post engagement, content categories and other variables gathered from this data. This would give companies insights into their competitors’ strategies and help measure the effectiveness of their own approach to marketing.

Use this dataset to track trends in user reactions and review topics over time; for example, analyse reactions by season or compare reaction rates between different content categories across 1-year period or more accumulated over months/years.

Utilize sentiment analysis techniques (like Natural Language Processing) over textual data contained for each post to build a better understanding of how consumers view the services being offered by these companies. This could be very useful in identifying challenges being faced by businesses as well as emerging opportunities in the local market landscape which they can leverage upon to generate revenue growth

Acknowledgements

If you use this dataset in your research, please credit the original authors.
Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: tourism_amazon.csv

Column name	Description
status_message	The text content of the post. (String)
link_name	The name of the link associated with the post. (String)
status_type	The type of post (e.g. link, photo, video). (String)
status_link	The URL of the post. (String)
status_published	The date and time the post was published. (DateTime)
num_reactions	The total number of reactions to the post. (Integer)
num_comments	The total number of comments on the post. (Integer)
num_shares	The total number of shares of the post. (Integer)
num_likes	The total number of likes on the post. (Integer)
num_loves	The total number of loves on the post. (Integer)
num_wows	The total number of wows on the post. (Integer)
num_hahas	The total number of hahas on the post. (Integer)
num_sads	The total number of sads on the post. (Integer)
num_angrys	The total number of angrys on the post. (Integer)
num_special	The total number of special reactions on the post. (Integer)
category	The content category of the post. (String)

Acknowledgements

If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .

Tables

Tourism Amazon

@kaggle.thedevastator_facebook_posts_of_amazon_tourism.tourism_amazon

220.19 KB
1018 rows
17 columns


CREATE TABLE tourism_amazon (
  "status_id" VARCHAR,
  "status_message" VARCHAR,
  "link_name" VARCHAR,
  "status_type" VARCHAR,
  "status_link" VARCHAR,
  "status_published" TIMESTAMP,
  "num_reactions" DOUBLE,
  "num_comments" DOUBLE,
  "num_shares" DOUBLE,
  "num_likes" DOUBLE,
  "num_loves" DOUBLE,
  "num_wows" DOUBLE,
  "num_hahas" DOUBLE,
  "num_sads" DOUBLE,
  "num_angrys" DOUBLE,
  "num_special" DOUBLE,
  "category" DOUBLE
);