How do you maximize the reach and engagement of your tweets? This is a hugely important question for companies who want to maximize the value of their content. There are even start-ups, like Social Flow, that specialize in optimizing the “engagement” of tweets by helping to time them appropriately. A growing body of research is also looking at what factors, both of the social network and of the content of tweets, impact how often tweets get retweeted. For instance, some of this research has indicated that tweets are more retweeted when they contain URLs and hashtags, when they contain negative or exciting and intense sentiments, and when the user has more followers. Clearly time is important too and different times of day or days of week can also impact the amount of attention people are paying to social media (and hence the likelihood that something will get retweeted).
But aside from the obvious thing of growing their follower base, what can content creators like news organizations do to increase the retweetability of their tweets? Most news organizations basically tweet out headlines and links to their stories. And that delicate choice of words in writing a headline has always been a bit of a skill and an art. But with lots of data now we can start being a bit more scientific by looking at what textual and linguistic features of headlines tend to be associated with higher levels of retweets. In the rest of this post I’ll present some data that starts to scratch at the surface of this.
I collected all tweets from the @nytimes twitter account between July 1st, 2011 and Sept. 30th, 2011 using the Topsy API. I wanted to analyze somewhat older tweets to make sure that retweeting had run its natural course and that I wasn’t truncating the retweeting behavior. Using data from only one news account has the advantage that it controls for the network and audience and allows me to focus purely on textual features. In all I collected 5101 tweets, including how many times each tweet was retweeted (1) using the built-in retweet button and (2) using the old syntax of “RT @username”. Of these tweets, 93.7% contained links to NYT content, 1.0% contained links to other content (e.g. yfrog, instagram, or government information), and 0.7% were retweets themselves. The remaining 4.6% of tweets in my sample had no link.
The first thing I looked at was what the average number of retweets was for the tweets in each group (links to NYT content, links to other content, and no links).
- Average # of RTs for tweets with links to NYT content: 48.0
- Average # of RTs for tweets with links to other content: 48.1
- Average # of RTs for tweets with no links: 83.8
This is interesting because some of the best research out there suggests that tweets WITH links get more RTs. But I found just the opposite: tweets with NO LINKS got more RTs (1.74 times as many on average). I read through the tweets with no links (there’s only 234) and they were mostly breaking news alerts like “Qaddafi Son Arrested…“, “Dow drops more than 400 points…“, or “Obama and Boehner Close to Major Budget Deal…“. So from the prior research we know that for any old tweet source, URLs are a signal that is correlated with RTs, but for news organizations, the most “newsy” or retweetable information comes in a brief snippet, without a link. The implication is not that news organization should stop linking their content to get more RTs, but rather that the kind of information shared without links from news organizations (the NYT in particular) is highly retweetable.
To really get into the textual analysis I wanted to look just at tweets with links back to NYT content though. So the rest of the analysis was done on the 4780 tweets with links back to NYT content. If you look at these tweets they basically take the form: <story headline> + <link>. I broke the dataset up into the top and bottom 10% of tweets (deciles) as ranked by their total number of RTs, which includes RTs using the built-in RT button as well as the old style RTs. The overall average # of RTs was 48.3, but in the top 10% of tweets it was 173 and in the bottom 10% it was 7.4. Here’s part of the distribution:
Is length of a tweet related to how often it gets retweeted? I looked at the average length of the tweets (in characters) in the top and bottom 10%.
- Top 10%: 75.8 characters
- Bottom 10%: 82.8 characters
This difference is statistically significant using a t-test (t=5.23, p < .0001). So tweets that are in the top decile of RTs are shorter, on average, by about 7 characters. This isn’t prescriptive, but it does suggest an interesting correlation that headline / tweet writers for news organizations might consider exploring.
I also wanted to get a feel for what words were used more frequently in either the top or bottom deciles. To do this I computed the frequency distribution of words for each dataset (i.e. how many times each unique word was used across all the tweets in that decile). Then for each word I computed a ratio indicating how frequent it was in one decile versus the other. If this ratio is above 1 then it indicates that that word is more likely to occur in one decile than the other. I’ve embedded the data at the end of this post in case you want to see the top 50 words ranked by their ratio for both the top and bottom deciles.
From scanning the word lists you can see that pronouns (e.g. “I, you, my, her, his, he” etc.) are used more frequently in tweets from the bottom decile of RTs. Tweets that were in the top decile of RTs were more likely to use words relating to crime (e.g. “police”, “dead”, “arrest”), natural hazards (“irene”, “hurricane”, “earthquake”), sports (“soccer”, “sox”), or politically contentious issues (e.g. “marriage” likely referring to the legalization of gay marriage in NY). I thought it was particularly interesting that “China” was much more frequent in highly RTed tweets. To be clear, this is just scratching the surface and I think there’s a lot more interesting research to do around this, especially relating to theories of attention and newsworthiness.
The last bit of data analysis I did was to look at whether certain parts of speech (e.g. nouns, verbs, adjectives) were used differently in the top and bottom RT deciles. More specifically I wanted to know: Are different parts of speech used more frequently in one group than the other? To do this, I used a natural language processing toolkit (NLTK) and computed the parts of speech (POS) of all of the words in the tweets. Of course this isn’t a perfect procedure and sometimes the POS tagger makes mistakes, but I consider this analysis preliminary. I calculated the Chi-Square test to see if there was a statistical difference in the frequency of nouns, adverbs, conjunctions (e.g. “and”, “but”, etc.), determiners (e.g. “a”, “some”, “the”, etc.), pronouns, and verbs used in either the top or bottom 10% of RTs. What I found is that there is a strong statistically significant difference for adverbs (p < .02), determiners (p < .001), and verbs (p < .003), and somewhat of a difference for conjunctions (p = .06). There was no difference in usage for adjectives, nouns, or pronouns. Basically what this boils down to is that, in tweets that get lots of RTs, adverbs, determiners (and conjunctions somewhat) are used substantially less, while verbs are used substantially more. Perhaps it’s the less frequent use of determiners and adverbs that (as described above) makes these tweets shorter on average. Again, this isn’t prescriptive, but there may be something here in terms of how headlines are written. More use of verbs, and less use of “empty” determiners and conjunctions in tweets is correlated with higher levels of retweeting. Could it be the case that action words (i.e. verbs) somehow spur people to retweet the headline? Pinning down the causality of this is something I’ll be working on next!
Here are the lists of words I promised. If you find anything else notable, please leave a comment!