These are just some of the questions that become explorable once we connect social and television media. How might we tractably search television news for tweets? Given that tweets are often displayed alongside the user's Twitter handle, what if we simply searched the OCR'd onscreen text of each second of airtime for Twitter handles and then searched that text against the tweets made by that user?
To explore the idea of scanning television news for tweets, we conducted a pilot analysis involving tweets by Donald Trump and Joe Biden given their outsized potential influence in setting the news agenda, especially around public health issues like COVID From the VGEG 2.
Using an archive of Donald Trump's tweets since from the Trump Twitter Archive website we used an automated approach to link each onscreen tweet appearance with the actual tweet in question, while for the much smaller number of Biden tweets we manually connected them.
The end result is a second-by-second chronology of airtime across CNN, MSNBC and Fox News this year displaying tweets from either candidate, with the onscreen appearance connected back to the actual tweet! With the caveats above in mind, we're tremendously excited to see what researchers are able to do with this pioneering new dataset.
Most importantly, we hope this small experiment inspires a new way of thinking about the social-mainstream divide and leads to further research in automated scanning of television news for onscreen display of social media posts. Using the following query, we selected all , seconds of airtime as of midday Sept. We then downloaded an archive of his tweets since from the Trump Twitter Archive website. This archive contains the majority of his tweets, though some deleted tweets may be missing.
To connect the two datasets, we used the following algorithm see the PERL script for the full algorithm :. While it is possible that there are multiple people publishing tweets on this account for each type of device, the result here shows a very different tone for tweets being sent out by the different devices.
The term document matrix represents the text as a table whose columns are binary variables that correspond to the words used in the analysis. Each row represents one of the text responses or tweets, and each column represents one of the words by taking a value of 1 when the word is present in the text for that row, and a value of 0 when it is not.
This is a way of communicating the outcomes of the text setup and cleaning phase into other algorithms. If you want to design your own custom analysis, it can be useful to have the term document matrix computed explicitly within your project, and this can be done using Text Analysis - Advanced - Term Document Matrix. The automatic coding tool that is described below uses the term document matrix explicitly as one of its inputs. The predictive tree computes the term document matrix in the background for its own calculation, and does not rely on the presence of the term document matrix as an item in the report.
Note that the original version of the term document matrix shown in the webinar displayed the full contents of the term document matrix as a table. This turned out to be an inefficient way to store this data, particularly for larger data sets, and so the term document matrix now displays information about the underlying matrix rather than displaying it's contents in full.
This is similar to Machine Learning - Classification And Regression Trees CART , which is designed for creating a predictive tree between variables in the data file as opposed to using the text. In this case study, we used the favoriteCount as the Outcome , or variable to be predicted. Each branch of the tree shows where the presence of a particular word in a tweet predicts a much higher or lower average number of favorites. The width of each branch of the tree shows how many tweets are included in that part of the sample, and the color of the branch indicates the average value of the outcome variable - with darker reds indicating low average values, and lighter reds and blues indicating higher average values.
The tree diagram is interactive. If you hover your mouse over a node you get additional information about the sample and outcome variable for that node, and you can click on the nodes to hide or show that part of the tree. The tree shows significantly high numbers of favorites for tweets which talk about Hillary Clinton, and even higher average favorite count for those tweets which use the words hillary and spending.
Similar high scores were observed for tweets containing words bernie , law , and united. Jump to: navigation , search. Tweet Timeline Tweet timeline shows the number of Tweets posted by the Twitter account in chronological order. Following are the applications of a Tweet timeline: You can see when was the last time the Twitter account was most active You can see the patterns in tweeting to know whether the Twitter account is a bot or a real person You can see how active Twitter account is and how often does it tweet.
Client Source The Client source shows the variety of devices used by the Twitter account to post the tweets. It has the following applications: Know which device the Twitter Account uses most often Know the number of tweets posted by each Device Know whether the Twitter account is using any commercial platform like Hootsuite or Buffer to tweet.
Following are the applications of this graph: Know at what time of the day and on which day of the week the Twitter account is most active If you want to connect to the user, you can easily see that at which time the Twitter user is most active on Twitter.
Best Time to Tweet This predictive analytic calculates the best time at which the Twitter user should tweet to get the maximum exposure and retweets. Following are the applications of this analytics: Know the top 6 best time duration to tweet Know when the Twitter account is receiving maximum retweets per tweet. Most Mentioned Keywords and Usernames Those keywords and usernames which the Twitter user has mentioned the most in their tweet contents.
This analytic helps to: Know the interest of Twitter account Know the type of language the Twitter account is using Know whether the Twitter account is promoting something Know the most frequently contacted Twitter users of a targeted account.
Top Retweeted and Liked Tweets Get a list of the tweets of the Twitter user which received most retweets and likes. Here are its applications: Know what kind of tweets are receiving the maximum response Based on tweet content, you can identify those factors which helped you gain that many retweets and likes.
Those factors may be hashtags, images, videos or anything else Know average retweets per tweet and average likes per tweet. Separate Image and Video Files To make it more convenient for our customers to find the images and videos used by the Twitter accounts in their tweets, we have come up with separate images and video file: These files contain URLs of all the images and videos which are used in the Tweet content by the Twitter account.
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