Creating networks and key term/user lists from Twitter (Advanced)

The basic (much simpler) instructions are here.

**Please see also a PowerPoint presentation from Kim Holmberg summarising ways of creating Twitter Networks in Webometric Analyst (please save as filetype .pptx rather than .zip to view it)**

These notes summarise how to identify the key terms and users in a set of twitter data and how to create two types of network from the tweets collected. The notes describe how to create the networks from Tweets that have already been collected by you, so the first step is to collect the data using Webometric Analyst (see below for important advice) or another tool. The notes here describe how to create the following types of information.

Summary of key steps

Collecting tweets

The tweets should have been collected either by listing users to follow or keywords to search for in tweets, or both. It is no longer possible to download tweets with Webometric Analyst. There are some functions to analyse data collected before Twitter/X stopped free data sharing..

Identifying key terms and/or users for the tweets

Webometric Analyst can identify the most important words, hashtags and users based upon their relative frequency in texts for each label. For each label, the most important terms are those that occur frequently for that label and rarely for other labels. Webometric Analyst uses the chisquare metric to estimate the importance of terms. It will produce a list of terms for each label and their importance rating, as follows.

If you only have one set of queries then it is impossible to identify the most important terms because a comparison is needed for this. Instead you can follow the above procedure but sort on the raw term frequencies instead of the chi-squared values. The top terms will be common words like "it" and "the" and you will have to manually identify topic-related words from the sorted list.

Example: The spreadsheet here covers words extracted from different scholarly disciplines or fields.

Creating co-mention networks for words, #hashtags or @users

Co-mention networks are networks based upon how often words, hashtags or users co-occur in tweets. For instance, if the data set contains the following three tweets:

Then in terms of the three types of co-mention:

To obtain a co-mention network of tweets, click the

ALTERNATIVE METHOD: To obtain co-mention networks of any type complete the following instructions.

Creating direct tweet networks for @users

Direct tweet networks are based upon how often tweets from one @user contain the names of other @users. For example, consider the tweets:

Then in terms of messaging:

To obtain direct tweet networks complete the following instructions.

Example: The network below was created from digital humanities tweets, using the option to ignore @ and # symbols when processing the data so hashtags, usernames and keywords are all mixed up. The network was drawn with webometric analyst and manually tidied up by moving nodes around to make the pattern clearer and recolouring the digitalhumanities node from blue to red.