Approaches to analyzing Twitter data


This topic describes how you might choose to analyze and explore a dataset containing social media data from Twitter.

For information on how to collect and import Twitter data—refer to Import from Twitter.

What do you want to do?


Explore Twitter data in Detail View

When you open the dataset in Detail View, you can visually explore it. You can also:

  • Use the sort or filter functions to see patterns in your data. For example, you can filter Tweets to only show those made by a specific user or during a specific date range.

  • Hide columns to limit the amount of data you are looking at—for example, you could hide the Tweet ID and Location columns.

  • Adjust the column width—drag the boundary on the right side of the column heading until the column is the width that you want..

  • Manually code Twitter data at nodes representing themes or cases representing people—refer to Basic Coding in dataset sources for more information.

  • Use automatic coding techniques to perform broad-brush coding of the Tweets—refer to Gather Tweets by Username, Hashtag or other predefined columns.

You can also run queries to find and code at themes in your data:

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Gather Twitter data over time

Each time you capture Twitter data, a new NCapture file is created. When you import NCapture files into your project, by default, any matching social media datasets are merged together.

The only time you can merge matching social media datasets is when you import from NCapture. If you choose not to merge matching social media datasets during import, then you will not be able to merge them later in NVivo.

Matching datasets do not need to have the same names. To be considered matching, the social media properties of the datasets need to be the same—for example based on the same hashtag search in Twitter.

Matching datasets captured at different times may include some of the same content. When matching Twitter datasets are merged, any duplicate content is removed.

For example, imagine that you capture Twitter data for the hashtag #climate on Monday and import the NCapture file into your project. Then, on Tuesday and again on Wednesday you also capture Tweets based on the hashtag #climate. When you import these NCapture files into your project, by default, the Tweets from Tuesday and Wednesday are merged together with the dataset from Monday to create a single dataset.

If you want to merge matching datasets, make sure the Merge matching social media datasets (including previously imported) check box is selected on the Import from NCapture dialog, otherwise new datasets will be created when you input subsequent NCapture files.

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Gather Tweets by Username, Hashtag or other predefined columns

Do you want to gather Tweets from a particular user or hashtag? You can use auto coding to gather Tweets from predefined columns—for example user or hashtag.

The table below is a simplified example of a dataset containing Twitter data.

The columns containing Username and Hashtags are classifying fields and the Tweet column is codable field. Whether the columns are codable or classifying is predetermined and cannot be changed.

Username Tweet Hashtags
Person1 Study: rising sea levels threaten island communities. #climate bit.lyxfgn6B climate
Person2 Record high temperatures recorded in #arctic due to #climate change. arctic
Person2 We need to act now to slow the effects of #climate change. climate

Gather Tweets for each user into a node.

If you auto coded this dataset by Username, you would create the following node hierarchy:

  • Cases

  • Twitter

  • Username

  • Person1

  • Person2

The case nodes (Person1 and Person 2) are classified as 'Twitter User' and information from the user's profile—for example, Bio and Number of Followers—is stored as attribute values.

Gather Tweets for each hashtag into a node

If you auto coded this dataset by Hashtag, you would create the following node hierarchy:

  • Nodes

  • Twitter

  • Hashtag

  • climate

  • arctic

NOTE You can choose to code based on other predefined columns—for example, Location or Tweet Type (Tweet/Retweet).

NVivo provides an Auto Code Assistant to guide you through the process of auto coding—refer to Automatic coding in dataset sources for more information.

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