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TEDTalks Tweets

Measuring Engagement and Influence

By Twitter [source]

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About this dataset

This dataset is an invaluable resource for exploring the type and extent of interactions taking place on social media platforms in relation to TEDTalks. With over 12,000 tweets containing more than 10 attributes each, researchers have a heightened potential to gain insight into their target audience's feedback, measure their engagement through likes, replies and retweets, and ultimately make the much-needed changes for improvement. Analyzing this information allows them to dive deeper into how users interact with TEDTalks posts across Twitter networks and evaluate the level of influence that each post has had in terms of publicity. The dataset contains tweets content, creation dates (UTC), like counts, media links contained within messages (e.g., photos), outlinks (URLs other than those used for media), quote counts (Retweets with comments - RTs from here on out), quoted tweet IDs which contain user IDs who experienced prior interactions with other users through Retweeting or Quote tweeting activities as well as reply count numbers , retweet count amounts , retwetted tweet ids along with URLs/links included in every message as well discovering something we haven't seen before called a 'Unique Tweet ID' along with Conversation ID values that provide further context surrounding these particular encounters on Twitter according to each response. By having access to all this data related to what people are saying about TEDTalks online can help broaden the awareness of topics trending at any given moment giving greater possibilities towards optimizing performance outcomes due viewers reception engagement thus driving better positive results!

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How to use the dataset

Introduction

Exploring Attributes

In this dataset, you'll find twelve individual attributes associated with each tweet. Here is a brief explanation of the major ones:

Content: The content can be used to identify topics associated with each tweet and gain insights into user behaviour by identifying its keywords.
Like Count: The like count can be used to track engagement rate of a particular tweet, and also gives an idea of public opinion towards tweets posted by users throughout their life cycle.
Media: This attribute tracks if any media was attached along with a post or not which could potentially give more understanding about its engagement rate or other related information like reach, seeding etc.
Outlinks: Outlinks are URLs shared within posts which could give a deeper understanding about what kind of resources people are looking for when they come across such posts online in general bahavioural analysis from one platform- othersocial media platforms as well or analyzing offtake towards other websites as well considering linked urls/pages present within every post which would help understand overall performance for suggested links or generated leads from them . Quote Count & Quoted Tweet: These two attributes are particularly helpful when conducting sentiment analysis, as they allow you to track how people feel about certain topics discussed in tweets - even those that have been quoted from other sources such as blogs or news articles! Reply Count & Retweeted Tweet : Reply count reveals directly proportional relationship between engagements vs replied counts & retweeted tweets help provide insights into viral trends based upon retweeting patterns & gives us some deeper level customer journeys perspective indirectly also if analyzed further using different sets of data say customer information availible through different CRMplatforms/databases etc..

Analyzing Data

With so many factors at play, analyzing data properly BEFORE making conclusions becomes all the more important. The process begins by cleaning unnecessary variables - including deleting duplicate entries (if any). Next step involves exploring the content columns for keyword extraction and identifying distinct trends amongst those generated words/phrases based on trends over a particular period (monthly/yearly). Additionally, analytics tools may be employed for better visuals regarding growth rate comparisons between outlinks usage numbers and retweets/likes generated over certain timeframe specified especially during specific marketing campaigns’ execution frames

Research Ideas

• Analysis of Engagement Drivers: This dataset can be used to effectively analyze the engagement drivers that are driving user interactions with TEDTalks content, such as the volume and types of likes, replies, and retweets they generate. By gaining insights into social media behaviour patterns and trends, researchers can modify content strategies to increase engagement on target platforms.

• Influencer Mapping: Using this dataset, researchers can map out influential factors amongst individuals who post about TEDTalks with the intention of building relationships with these influencers in order to help bolster their reach potential within social networks.

• Media Contained Tweet Analysis: Researchers could also analyze which type of media is posted along with a tweet that impacts the success or failure of a post by analyzing unique tweet data contained in this dataset such as link count, image count, video count and GIFs etc

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

Acknowledgements

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