All exports have the following filename convention: {dataset}-{startdate}-{enddate}-{query}-{exclude}-{from_user_name}-{exclude_from_user_name}-{from_user_lang}-{url_query}-{media_url_query}--{module_name}-{module_settings}-{dmi-tcat_version}.{filetype}
All statistics and activity metrics come as a .csv file which you can open in Excel or similar.
Here you can select how the statistics should be grouped:
Tweet stats
Contains the number of tweets, number of tweets with links, number of tweets with hashtags, number of tweets with mentions, number of retweets, and number of replies
Use: get a feel for the overall characteristics of you data set.
User stats (overall)
Contains the min, max, average, Q1, median, Q3, and trimmed mean for: number of tweets per user, urls per user, number of followers, number of friends, nr of tweets, unique users per time interval
Use: get a better feel for the users in your data set.
User stats (individual)
Lists users and their number of tweets, number of followers, number of friends, how many times they are listed, their UTC time offset, whether the user has a verified account and how many times they appear in the data set.
Use: get a better feel for the users in your data set.
Hashtag frequency
Contains hashtag frequencies.
Use: find out which hashtags are most often associated with your subject.
Hashtag-user activity
Lists hashtags, the number of tweets with that hashtag, the number of distinct users tweeting with that hashtag, the number of distinct mentions tweeted together with the hashtag, and the total number of mentions tweeted together with the hashtag.
Use: explor user-hashtag activity.
Twitter client (source) frequency
Contains source frequencies.
List the frequency of tweet software sources per interval.
Twitter client (source) stats (overall)
Contains the min, max, average, Q1, median, Q3, and trimmed mean for: number of tweets per source, urls per source
Use: get a better feel for the sources in your data set.
Twitter client (source) stats (individual)
Lists sources and their number of tweets, retweets, hashtags, URLs and mentions.
Use: get a better feel for the sources in your data set.
User visibility (mention frequency)
Lists usernames and the number of times they were mentioned by others.
Use: find out which users are "influentials".
User activity (tweet frequency)
Lists usernames and the amount of tweets posted.
Use: find the most active tweeters, see if the dataset is dominated by certain twitterati.
User activity + visibility (tweet+mention frequency)
Lists usernames with both tweet and mention counts.
Use: see wether the users mentioned are also those who tweet a lot.
Identical tweet frequency
Contains tweets and the number of times they have been (re)tweeted indentically.
Use: get a grasp of the most "popular" content.
Word frequency
Contains words and the number of times they have been used.
Use: get a grasp of the most used language.
Media frequency
Contains media URLs and the number of times they have been used.
Use: get a grasp of the most popular media.
Export table with potential gaps in your data
Exports a spreadsheet with all known data gaps in your current query, during which TCAT was not running or capturing data for this bin.
Use: Gain insight in possible missing data due to outages
All tweet exports produces a .csv or .tsv file which you can open in Excel or similar.
Here you can select additional columns for the tweet exports (more = slower):
Random set of tweets from selection
Contains 1000 randomly selected tweets and information about them (user, date created, ...).
Use: a random subset of tweets is a representative sample that can be manually classified and coded much more easily than the full set.
Export all tweets from selection
Contains all tweets and information about them (user, date created, ...).
Use: spend time with your data.
List each individual retweet
Lists all retweets (and all the tweets metadata like follower_count) chronologically.
Use: reconstruct retweet chains.
Warning: This script is slow. Small datasets only!
Only tweets with lat/lon
Contains only geo-located tweets.
Export tweet ids
Contains only the tweet ids from your selection.
Export hashtag table (tweet id, hashtag)
Contains tweet ids from your selection and hashtags.
Export mentions table (tweet id, user from id, user from name, user to id, user to name, mention, mention type)
Contains tweet ids from your selection, with mentions and the mention type.
All network exports come as .gexf or .gdf files which you can open in
Gephi or similar.
Social graph by mentions
Produces a
directed graph based on interactions between users. If a users mentions another one, a directed link is created.
The more often a user mentions another, the stronger the link ("
link weight"). The "count" value contains the number of tweets for each user in the specified period.
Use: analyze patterns in communication, find "hubs" and "communities", categorize user accounts.
Social graph by in_reply_to_status_id
Produces a
directed graph based on interactions between users. If a tweet was written in reply to another one, a directed link is created.
Use: analyze patterns in communication, find "hubs" and "communities", categorize user accounts.
Co-hashtag graph
Produces an
undirected graph based on co-word analysis of hashtags. If two hashtags appear in the same tweet, they are linked.
The more often they appear together, the stronger the link ("
link weight").
Use: explore the relations between hashtags, find and analyze sub-issues, distinguish between different types of hashtags (event related, qualifiers, etc.).
»
launch (set minimum frequency)
Bipartite hashtag-user graph
Produces a
bipartite graph based on co-occurence of hashtags and users. If a user wrote a tweet with a certain hashtag, there will be a link between that user and the hashtag.
The more often they appear together, the stronger the link ("
link weight").
Use: explore the relations between users and hashtags, find and analyze which users group around which topics.
Bipartite hashtag-mention graph
Produces a
bipartite graph based on co-occurence of hashtags and @mentions. If an @mention co-occurs in a tweet with a certain hashtag, there will be a link between that @mention and the hashtag.
The more often they appear together, the stronger the link ("
link weight").
Use: explore the relational activity between mentioned users and hashtags, find and analyze which users are considered experts around which topics.
Bipartite hashtag-source graph
Produces a
bipartite graph based on co-occurence of hashtags and "sources" (the client a
tweet was sent from is its source) . If a hashtag is tweeted from a particular client, there will be a link between that client and the hashtag.
The more often they appear together, the stronger the link ("
link weight").
Use: explore the relations between clients and hashtags, find and analyze which clients are related to which topics.
Bipartite user-source graph
Produces a
bipartite graph based on co-occurence of users and "sources" (the client a
tweet was sent from is its source) . If a users tweets from a particular client, there will be a link between that client and the user.
The more often they appear together, the stronger the link ("
link weight").
Use: explore the relations between clients and users, find and analyze which users use which clients.
Cascade
The cascade interface provides a ground level view of tweet activity by charting every single tweet in the current selection. User accounts are distributed vertically; tweets - shown as dots - are spread out horizontally over time. Lines indicate retweets.
Use: visually explore temporal structures and retweets patterns.
Warning: This view requires a large screen and is limited to (very) small data selections.
The Sankey Maker
Use: plot the relation between various fields such as from_user_lang, hashtags or Twitter client.
Associational profile (hashtags)
Produces an associational profile as well as a time-encoded co-hashtag network.
Use: explore shifts in hashtags associations.
Modulation Sequencer (URL)
The tool allows one to qualitatively examine how a URL is shared on Twitter over time. See Moats and Borra (2018) for a full explanation.
Use: enter a (part of a) URL in the data selection field at the top and click 'update overview'. Then launch this tool.