Archive for the ‘politics’ tag
Update: Twitter was right. It’s a Romney-Ryan ticket.
It seems very likely that Mitt Romney is going to select Paul Ryan as his running mate for the Republican Presidential nomination. And it’s looking like Twitter predicted this. We hinted that we’d been tracking Republican VP Candidates with the screenshot accompanying the announcement of our new dashboard earlier this week. As you can clearly see from the updated dashboard below, Ryan started to pull away from the potential VP pack three days ago in terms of unique reach on Twitter. Of the pool of likely candidates, Ryan’s seen the greatest increase in reach over the past month, gaining a 65% increase in reach in the past 30 days. In addition, he’s seen the largest gains in both the number of total tweets and unique people talking about him recently.
So, did Twitter predict Romney’s decision correctly? Well, we’ll know soon enough, as Romney is expected to officially announce his vice presidential running mate tomorrow. We find Twitter’s potential to predict (or not) cultural and current events very interesting, so we’ll be following along and will post a more in-depth analysis next week, so more very soon.
At TweetReach, we’re often asked about how to measure share of voice (SOV). Measuring share of voice involves comparing one brand’s metrics to the total conversation about that brand’s category. Historically, this has been a difficult exercise because high quality data is hard to come by. But Twitter is an abundant and accessible source of real conversational data, allowing us to easily track mentions across a variety of brands. You can now determine the size of conversation for an entire category and compare your own brand to the overall conversation.
You can measure share of voice for any set of similar topics - competitive brands, products, companies or people. You can even compare share of voice across political candidates. Political candidates are the perfect example for a share of voice comparison. There are usually several people in the race, with a few frontrunners and a few hangers-on, just like most any product or business category. And people are talking about them on Twitter, providing a remarkable dataset for analysis.
Earlier this year, we tracked tweets about the U.S. Republican presidential candidates (see our interactive visualization and analysis). Now that Mitt Romney has emerged as the presumptive GOP nominee, we’re tracking the candidates for the vice presidential slot on the Republican ticket. VP candidates are not elected separately, but we can still use Twitter to gauge popular opinion and awareness on these candidates. Plus, they make a great example for a blog post about share of voice.
So, here are four steps to using Twitter data to measure share of voice.
1. Decide who you want to compare.
Before you start measuring, you’ll need to determine which competitors to compare to your own brand. What are the brands that make up the category you’re interested in measuring? Pick two to ten to compare. It’s probably easy to pick out your one or two most direct competitors, but also consider other less obvious choices you should add, as well as any large brands that make up your category. It’s possible that what your customers perceive as related might not even be on your radar, so think about this carefully.
In our Republican vice presidential candidate tracking, picking who to track was not that difficult. There are a set of people who have publicly made some indication that they’re interested in the job, and others that analysts and others who pay attention to these kinds of matters think could be chosen. So after a little research, we narrowed our field of possible candidates to 10 people:
- Kelly Ayotte
- Jeb Bush
- Chris Christie
- Bobby Jindal
- Bob McDonnell
- Tim Pawlenty
- Rob Portman
- Marco Rubio
- Paul Ryan
- John Thune
There are probably a few others we could include (or remove), but this is a solid, representative list for our needs. However you choose, pick 2-10 related brands to monitor in addition to your own.
2. Set up appropriate keywords for tracking.
Next, you need to track comparable terms for all brands. Most Twitter measurement tools (TweetReach included) will require a set of queries or keywords to begin tracking tweets. In this step, your goal is to make sure that your metrics aren’t later impacted by a data quality issue. If you monitor one brand’s Twitter account, then monitor all brands’ Twitter accounts. You probably know all the keywords you’d want to track for your brand, so think as carefully about the others as you did your own. Are you using common misspellings or nicknames? Are there other languages to consider? Multiple official Twitter handles or hashtags?
In this GOP VP case, we’re tracking full names (“Marco Rubio”) and Twitter handles (@marcorubio) for all candidates. We opted not to add last name-only keywords since candidates like Jeb Bush and Paul Ryan have fairly common last names and that would result in more noise than useful data. Since we can’t track their last names, we won’t track any other last names either. You can decide what makes sense given your goals, but just be consistent across all brands.
3. Collect enough data.
The next step is to start collecting data. Some tools do this in real time, and others have historical data you can mine. Either way, collect enough data that it’s representative of the full spectrum of conversation about your brands. Conversations can be spiky over short periods of time, so it’s best if you have weeks or preferably months to balance out those spikes across all brands. A longer data collection period also allows you to notice trends in SOV changes. The more data, the better. The longer you’ve been collecting data, the better.
GOP VP candidates see jumps in Twitter mentions when they’re featured in the news or after a public appearance. Some will just see more total tweets over time. We want to track long enough that we can differentiate between a legitimately higher metric and a one-time spike. In our specific case, we’ve only been tracking these candidates since early May (so just over two weeks) and the data is still pretty immature. Some of the candidates have been added even more recently than that, so their data is newer still. This means we shouldn’t take any of these metrics too seriously yet. But they will improve over time, so when we check in next month, we’ll have a much more representative picture of the true conversation.
4. Compare several metrics.
Finally, it’s important to compare brands across several different metrics to truly understand what’s going on. You may have a favorite stat or a particular KPI you’re targeting, but try to compare a few different metrics before deciding which to use moving forward. One brand might have a high reach, while another could have a lot of tweets. Use several metrics to compare, to see where the patterns are and what metrics make most sense in your industry or category.
Let’s look at a few metrics for the current top three Republican VP candidates (at least according to Twitter): Chris Christie, Marco Rubio, and Paul Ryan. This list will likely change as our data matures, but it’s fine for an early analysis.
One of our favorite metrics to start with is simple tweet volume. Tweet counts are useful in understanding the size of the conversation about a candidate. Below are graphs for both tweets per day and cumulative tweets so far this month for the three candidates.
You can see that Ryan (yellow) is slightly ahead of Christie (blue) in cumulative tweets right now, but both are increasing steadily. Christie has had two large spikes in daily tweet volume, while Ryan has had one. Both of these metrics will stabilize after a few more weeks, and we’ll have a clearer picture of who’s on top. Right now, I’d say Ryan has the slight edge on Christie, but it’s close.
And if we’re actually going to look at share of voice, let’s compare each candidate’s tweet volume to overall tweet total. In the past two weeks, there have been 46K total GOP VP candidate tweets. 35.2% of those mentioned Ryan, with Christie close behind at 32.9%. Track SOV over time, as changes in a brand’s share could indicate important perception shifts. For example, when we started tracking GOP presidential candidates in early January, Ron Paul dominated that conversation’s share of voice, and was mentioned in more than 40% of all tweets. But by April, that share had dropped off almost entirely, leaving the rest to Mitt Romney.
It’s also helpful to look at several metrics side-by-side. In this case, let’s compare reach, tweet volume and number of unique contributors.
Looking across these three metrics, Christie appears to be the frontrunner. His reach is currently more than 15 million, with 10 million for Rubio and 8 million for Ryan. Looking at reach and tweet volume in conjunction with contributors – the number of unique people talking about a candidate on Twitter – it seems like a lot of different people are talking about Christie and Ryan, while Rubio has a smaller group of vocal supporters. To achieve a 50% higher reach when compared to the other candidates, Christie was probably mentioned by a celebrity, typically the only people to have follower counts over a few million. (In this case, it turns out @jimmyfallon, who has 5.5 million followers, tweeted publicly to the governor.)
Reach is an excellent metric for share of voice, because it tells you about the size of the potential audience for a brand. The bigger the reach, the larger the variety of people who are spreading the message. A high reach indicates a diversity in contributors and audience, as well as some potentially influential and high-follower contributors.
We also recommend unique contributors as a share of voice metric. Which brand has more different people talking about it? One caveat about both reach and contributors is that since these are metrics based on counting uniques, you can’t compare one brand’s metric to an overall sum, since you can add up reach or contributor numbers to get overall reach or contributors. You can only compare reach to another brand’s reach. That’s still useful, but may not be a traditional share of voice metric.
Twitter and share of voice
Twitter is a incredibly rich source of share of voice data. If you’re tracking similar brands, products or people and one has an audience on Twitter, it’s likely they all will. Due to the real-time, public and archivable nature of Twitter, we can access this data for all kinds of useful analyses. People can and do talk about their favorite – and least favorite – brands on Twitter. For all these reasons, Twitter is perfect for SOV analysis, if you do it right. Doing share of voice right means selecting the appropriate brands to compare, ensuring consistency in search queries, aiming for long-lived data collection, and embracing diversity in data analysis.
When the 2012 United States Republican presidential primaries and caucuses began back in January, we took a look at whether Twitter activity could be used as a predictor of the elections. We started tracking all tweets that mentioned any of a candidate’s Twitter accounts (personal and campaign) and based on the Twitter activity coming out of the Iowa caucuses, we saw that Twitter activity was less an indicator of the outcome, and more a reflection of the overall conversation happening around the candidate. Reach, exposure, and activity were largely driven by mentions by popular news and media accounts, many of which have significant numbers of followers and retweets.
Since January, the Twitter activity on the candidates has been staggering – some of the largest reach and exposure we’ve ever tracked, with over 8 million tweets from hundreds of thousands of contributors. These contributors reached more than 120 million unique Twitter accounts and generated almost 22 billion impressions.
Right before the Super Tuesday primaries in March, we launched the TweetReach Republican Primary Tracker which looked at the relationship between what people say on Twitter and what they do at the polls. In the visualization, we mapped the number of unique Twitter users talking about a candidate to the y-axis, polling results to the x-axis, and tweet volume to the circle radius.
While, based on our previous analysis, we did not believe Twitter conversations could predict winners, we thought it would be interesting to see what tweets can tell us about how potential voters feel about the candidates. The visualizer confirmed that despite a candidate’s tweet volume, reach, and exposure on Twitter, these data were not a good predictor of election results. They are, however, a great way to understand how popular dialogue about a candidate translates into Twitter conversation.
Today, with Rick Santorum bowing out of the race, we took another look and found that Twitter conversation about Santorum had been relatively quiet since Super Tuesday but, as expected, spiked with today’s news as people came out of the woodwork to Tweet about the candidate.
In fact, a full 21% of Rick Santorum’s exposure since Super Tuesday (over 368 million impressions) occurred today after the announcement. When viewed with the TweetReach Republican Primary Tracker, the impact of the conversation around Santorum’s departure is even more pronounced.
We look forward to tracking the upcoming full election. In the meantime, we’d love to know what you think!
Here in the United States, we’re right in the middle of the Republican primaries as the country tries to decide who the GOP nominee for President will be in our election later this year. One of the more interesting conversations around the 2012 Presidential election is the relationship between what people say on Twitter and what they do at the polls. Can we use Twitter conversations to predict election winners? Or, if they can’t predict results, what can tweets tell us about how potential voters feel about the candidates?
With Super Tuesday approaching and the GOP candidate field still wide open, we’ve been tracking tweets about the six top candidates for the Republican Presidential nomination since January 1 – Newt Gingrich, Jon Huntsman, Ron Paul, Rick Perry, Mitt Romney, and Rick Santorum. From those tweets, we built an interactive visualization of how Twitter talks about the GOP candidates, and how that relates to poll numbers over time.
Check out our interactive Republican primary Twitter tracker here or click on the screenshot below.
To create this visualization, we’re using a set of TweetReach Pro Trackers to track Twitter conversation about each of the candidates, along with our API to update the visualization daily. In the visualization, we’ve mapped the number of unique Twitter users talking about a candidate to the y-axis, polling results to the x-axis, and tweet volume to the circle radius. Polling data is from RealClearPolitics.
We often use TweetReach to track the success rates of TV shows and other major media events. We thought it would be interesting to analyze the tweets during last night’s Iowa Caucuses for the Republican nomination for President. As you know by now, former Massachusetts Governor Mitt Romney beat Rick Santorum by just a few votes, and Ron Paul came in third. Could Twitter activity have been used to predict the winner?
Last Friday, we started tracking all tweets that mentioned any of a candidate’s Twitter accounts (personal and campaign), the major news networks’ coverage of the caucuses, and hashtags such as #iacaucus that were used by the major news media and others in their tweets.
Interestingly, the overall Twitter volume about the caucuses was pretty low. In fact, we often track more tweets in an hour about a single TV show than we have in five days about all nine candidates. Nevertheless, early on in the evening we predicted a win by Mitt Romney or Ron Paul based on early Twitter activity and retweets.
Overall tweet volume, the number of unique contributors (people who have tweeted about a topic), reach, exposure, and the retweet rate (average number of retweets per tweet) can be useful indicators for deciding what topics are most popular on Twitter. But can they help predict results in Iowa? Here’s how the data shook out for the six major candidates:
Based on overall reach, Romney, Santorum, and Paul came in as the top three candidates, mapping directly to the final caucus results. Based on this analysis, reach seems to be a good indicator of success. But, since much of this reach can be attributed to mentions by major news media accounts, it’s more likely that Twitter activity is merely descriptive of what is happening. Nevertheless, the percentage of total reach from the major candidates ended up being very close to the actual caucus results:
Also noteworthy, despite having over 2.5x the tweet activity of Romney or Santorum, Ron Paul only had the third highest reach. Paul also had over 1.5x the contributors and the highest retweet rate of the candidates, more likely an indication of his support among younger voters and their engagement on Twitter. But, a larger follower count and more activity on Twitter don’t necessarily help predict a winner.
Other fun facts, the most retweeted tweet in our analysis came from Ron Paul’s account, and mentions Jon Huntsman who didn’t actively campaign in Iowa:
And, the second-most retweeted tweet came from Robert Reich, professor at University of California at Berkeley and former United States Secretary of Labor:
Studies have shown that Americans use social media to follow politics. As the primary season unfolds, we’ll continue to analyze the Twitter activity of the major candidates and report back on what we find. In the mean time, we’d love your feedback!
President Obama held his first-ever Twitter Townhall today. For several days, White House staffers have been collecting questions from the public on Twitter. Anyone could contribute a question for the President by just adding the #AskObama hashtag to a tweet.
We followed all the #AskObama tweets during today’s Q&A session. During the hour-long event, we tracked 64,789 tweets from 29,772 contributors with a reach of 35 million. There were more than 161K total tweets posted yesterday with a daily overall reach of 49.5 million. Here’s a word cloud of those tweets (thanks, Wordle!).
We wanted to understand just how many tweets were posted about some of those topics. Nearly a quarter of all questions were related to jobs and unemployment, about 18% related to the economy, 10% about taxes, and 5% about education. Of course, not all questions were about serious topics like jobs and the economy. More than 100 people asked if the president prefers boxers or briefs, and 200 asked the president to bring back Arrested Development (or to hurry the movie along). And there were more than 1,000 retweets of the Nyan Cat.
Finally, here are a few of our favorite less-than-serious questions. We’re still wondering about the answer to the third question ourselves. And of course we all know the answer to that last one.