Trump vs Cruz AND Clinton vs Sanders

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Todd Murphy Vice President Universal Information Services news monitoring and PR measurementBy Todd Murphy, Vice President, Universal Information Services

With the Iowa Caucus upon us, I’m writing this post just hours before we learn which candidate may emerge with a chance to run for the party nomination. If you listen to the commercials one may think that every candidate is a hate mongering, incompetent person that lacks all styles of leadership. If one watched the many debates we have had so far, you’re undoubtedly left with at least two thoughts. First, there are too many Republican candidates to fit on one stage. Second, Donald Trump has the ability to drop a networks audience ratings and ad revenue by simply not showing up.

So the image of this crop of Republican and Democratic candidates may not differ from election cycles of the past. Campaigns have gone negative. People have made personal insults. But what may differ is the image of the candidates, and their ability to wag the dog (in this post the media is the dog). Ironically, the two front-runners, Clinton and Trump, have a greater negative volume of media mentions relative to positive mentions. In other words, their news is more often negative than positive. Whereas the reverse is true for Sanders and Cruz.

We’ve done some last minute media measurement that combines the top two candidates within each party. We have looked at their share of voice in the media, then overlaid article sentiment to determine which candidates appear to be the true front-runners heading into tonight’s Iowa Caucus. Based on our analysis we can make the following claims.

Between Donald Trump and Ted Cruz, Donald gets more media exposure. However, Cruz has more positive stories posted/written about him than Trump receives. Will the power of the positive overcome the volume of stories Trump has received?

On the democratic side, Hillary Clinton gets far more media coverage than Bernie Sanders. But Sanders articles are more positive than negative, whereas the reverse is true for Clinton.

We plan to follow up with a post looking at the media coverage posted after the Iowa Caucus process finishes. Before everything moves to New Hampshire, we should be able to see how media share of voice and article sentiment correlate to public opinion, specifically within the caucus process. Stay tuned, but also leave us your thoughts on what you think impacts the voters the most.

8 Comments

  1. Zack McNair on at 6:42 PM

    Very interesting. Have a couple questions: First, about your methodology – when you say “media” do you mean social media, traditional media, or both? If traditional media, would be interesting to see the breakdown by types of media, eg newspapers, tv, etc. Also, how many articles did your team measure, and was the dataset sampled? And what is the time period you’re looking at here? Thanks, Zack

    • Todd Murphy on at 8:41 PM

      Zack: Great questions! Media measurement is very much about following a strict methodology. Here are some details.
      Media: TV, Radio, newspapers, web sites, social, blogs and user generated mentions
      Sample size: We sampled approximately 9000 stories since yesterday, the past 24 hours.
      Dataset: US content only

      • Zack McNair on at 12:57 AM

        Hey, I appreciate the dialogue, Todd. I didn’t quite understand one thing – you sampled what percentage of 9,000 stories, or 9,000 was your sample set out of how many? And if US content only, how did you know that social posts, such as Twitter, originated from the US? Sorry, new to the field and just trying to get my head around it. Also, now that the results are in, I wonder where you guys saw Rubio in the dataset. Sounds like the GOP is now a three-man race.

        • Todd Murphy on at 11:26 AM

          I’m happy to answer your questions, but would enjoy the opportunity to discuss PR and media measurement in detail. Feel free to contact me through our site at https://universal-info.com . Regarding your questions, 9000 was the sample set out of “all available stories”. It’s impossible to know how many stories exist at any point in time because new ones are constantly being posted or published while old ones expire away. News is ephemeral. We normally analyze media exposure over time, collecting all stories that match a specific phrase or subject. For social media posts we can use geo-fencing based on the location someone includes in their portal. When the sample size is this large those that don’t have a location, or might be misleading, become statistically insignificant. Verifying geo-location is important. As for results, we are measuring the data now and will be posting a follow up post today. Thanks.

          • Zack McNair on at 1:17 PM

            Sounds good Todd, I’ll reach out to you guys soon. If you could just humor me for one or two more questions: 9,000 stories in the last 24 hours, wow! Can you point me in the direction of the software you used to perform your analysis (I’m in the market for a good SaaS social/news monitoring and analysis tool). And did you give each tweet the same amount of weight as a story from say, the New York Times? Finally, where did neutral coverage fit into this? The way it’s presented it looks like the coverage was strictly positive or negative, but I’m sure there were some posts that trended more neutrally. Thanks again, and I’ll be in touch!



          • Todd Murphy on at 2:07 PM

            We provide our own comprehensive media monitoring platform. With the ability to track and search TV, radio, print, web, and social content more comprehensively we are able to ensure a higher level of measurement accuracy. Again, something we would be happy to show you through a demonstration. Our PR Measurement Department validates all media exposure and applies a valid methodology for generating results. Our listening platform is more a Software with Service than a Software as a Service. Our clients need the highest level of accuracy when it comes to measurement, something that can’t be completely automated through SaaS platforms. Therefore, we combine the best of both worlds, automated tracking and reporting plus qualitative measurement. For this post we subtracted neutral coverage from the total volume of coverage for each condidate, then measured sentiment relative to their volume of coverage.



  2. Martin Pugh on at 7:22 PM

    Todd,
    Interesting comparative study and very timely! Will be exciting to see how the results pan out this evening.
    .~Martin

  3. Zack McNair on at 4:02 PM

    Great! Thanks again, and can’t wait to see the followup post.

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