Product

Analyzing Time & Volume Trends in News Content

Introduction

Our News API has a variety of analysis focused endpoints that allow our users to make sense of the data extracted from news content in a meaningful way. In this post we look at one in particular, /time_series, and how it can be used to uncover trends in news publication times relating to specific topics and keywords.

We will show you how to draw meaningful information from these trends that can help with a number of tasks, including planning of resources and deciding the best times to both publish content and distribute it on social media.

Most popular days to publish news content – Overall

To begin with, we thought it would be interesting to uncover the most popular days for posting news content by analyzing all of our News API sources over a 7 day period. This query returned all results without any search criteria. We grabbed everything that was available.

Looking at the graph below, we can see that publication volumes peak midweek with Tuesday, Thursday and Wednesday being the most popular days. Monday comes next, however only slightly behind.

We begin to see a clear decline in published stories as we approach the weekend, with a 17% drop from Thursday to Friday. The weekend is clearly the least popular time to publish news content, with a decrease in content publication in the region of 60-70% versus midweek days (Monday – Thursday).

Why?

You can probably think of many reasons why the majority of news content is published midweek, the most obvious being the traditional Monday-Friday work week which places many of us in front of computer screens during these days. However, the next graph hints at a possible reason.

The graph below represents social media shares by days of the week. We analyzed Facebook shares of our News API content for one week and produced the following graph.

As you can see, our two charts are almost identical, with the trend in Facebook shares almost matching the trend in content publication.

We believe this is not a coincidence. Publishers want to give their content the best possible chance of being shared and circulated on social media, so perhaps they are aligning their publication times with peaks in Internet user activity, which naturally corresponds with peaks in social media activity.

Now we know what days are most popular, let’s drill down a level and find out what times of the day are most popular.

Most popular time of day to publish content – Overall

Please note: For this analysis we used the GMT timezone.

The graph below represents a 24-hour period starting at 0 (00:00). As you can see, the peak time for content publication is 15:00, followed by the two preceding hours, 14:00 and 13:00.

Understandably, content publication tails off towards midnight and into the AM but it is worth noting the peaks at 20:00 and 22:00 and the sharp rise from 06:00 – 09:00.

By putting these peaks together, we can perhaps see a trend emerging;

  • 06:00 – 09:00 Breakfast/Commute
  • 13:00 – 15:00 Lunch
  • 19:00 – 20:00 Dinner
  • 22:00 Before bed

Think about the most common times that people check social media and this all starts to make sense!

 

But what if we want to be industry-specific with our analysis?

While overall publication patterns give interesting insights, we want to know when content is being published relating to specific people, organizations and keywords. Let’s take a look three popular topics to see if we can spot any interesting trends emerging on a daily, weekly or monthly basis.

 

Applying specific search criteria

In the hope of producing some varied and interesting insights into publication patterns we took three very different topics of interest and produced daily, weekly and monthly results using the Time Series endpoint. Here’s our three keywords;

  • Nasdaq (finance)
  • Samsung (technology)
  • NFL (sport)

Daily trends

Nasdaq and Samsung follow a very similar pattern with a peak on Tuesday followed by a consistent decline towards the weekend. Perhaps the most notable observation here is the comparatively high volumes of content published on Sundays versus the analysis we did on overall publications.

The NFL search gives us an interesting spike on Thursdays, which bucks the trend from what we have seen so far in our analysis. You don’t have to be a football fan to guess why this spike occurs – Thursday night football !

Although the majority of NFL games are played on Sunday (hence the high content volume on Monday for post-game analysis), publishers and marketers know how engaged their readers are with social media midweek and they certainly take advantage of this. Thursday Night Football involves just one game. There is usually 14 games played on a Sunday. That’s 4% more content published on a Thursday even though 86% of the weekly games are played on a Sunday. That’s the power of social media!

Hourly trends

We can now take a look at each of our three keywords and produce graphs to show trends in their publication times. We want to see;

  • Publication time, by keyword
  • EST timezone
  • with previous trends displayed

As you can see from the graph below, we have a blue line representing the average time and volume of publications relating to Nasdaq on a per day basis. The grey lines represent previous trends – these are individual days’ data that, when combined, produce our blue line average.

Why display previous trends?

Displaying previous trends enables us to see peaks and troughs in content publication relating to specific topics or keywords, This can be particularly useful when planning or budgeting for your API usage. For example, our News API enables you to cap your monthly usage at a volume that suits your needs and budget. Previous trends can help you decide what this cap should be, to ensure you do not miss important story content when a spike in publication occurs. You don’t have to hit your cap each month (you still only get charged for what you use) but it is always a good idea to have that ‘buffer’ in place should a spike occur.

 

Samsung

While the Nasdaq stock market operates in consistent patterns (as seen by our graph above) our graph for Samsung is a little less clear-cut! OK, it’s clearly a mess, but we can definitely see a trend towards publishing during the morning and into lunch, with a clear tail-off in volume after 1pm.

Again, if I was analyzing Samsung-related content it would be extremely useful to not only see the average trend spikes but also the maximum volumes that have been published during my specified time period.

 

NFL

Our NFL hourly analysis shows two very
distinct peaks at roughly 11:00 and 15:00. These peaks represent a 70-80% increase in content publication, versus average, in a single day. Sure, you won’t always know when a superstar quarterback is about to announce his retirement, but at least you can gauge the amount of content that will require analysis the next time such an event occurs.

Monthly trends

And finally, a quick look at monthly trends. We thought it was interesting to note the consistency with Nasdaq compared to the NFL, which appears to be more prone to irregular fluctuations in publication volumes. We can only speculate as to why this is the case, but perhaps some NFL games attract more media attention than others, while Nasdaq is consistently big news in the stock market world.

Conclusion

We hope this analysis has given you an insight into the level of useful data that can be achieved with the Time Series endpoint within the News API. Producing these trend graphs is step one in our overall analysis.

While the graphs and data mentioned above can be extremely useful in establishing volume trends over time, further research and analysis is required in uncovering why these trends are occurring and how best to take advantage of them. We’ll cover this in a follow-on post next week.

 




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Noel Bambrick

Customer Success Manager @ AYLIEN A graduate of the Dublin Institute of Technology and Digital Marketing Institute in Ireland, Noel heads up Customer Success here at AYLIEN. A keen runner, writer and traveller, Noel joined the team having previously gained experience with SaaS companies in Australia and Canada. Twitter: @noelbambrick