Twitter and Facebook are Rapidly Rising Across All Major US Demographic Groups as Primary News Platforms

"Media in Central Park New York City", Image by Ernst Moeksis

“Media in Central Park New York City”, Image by Ernst Moeksis

Cutting across five fundamental demographic segments, Twitter and Facebook are now the primary sources for news among the US population. This was the central finding of a new report issued on July 14, 2015 by the Pew Research Center for Journalism and Media entitled News Use on Facebook and Twitter Is on the Rise by Michael Barthel, Elisa Shearer, Jeffrey Gottfried and Amy Mitchell. The full text and supporting graphics appear in an 18-page PDF file on the Pew website is entitled The Evolving Role of News on Twitter and Facebook. I highly recommended clicking through to read the full report.

A number of concise summaries of it quickly appeared online. I found the one written by Joseph Lichtman on NeimanLab.com (a site about Internet journalism at Harvard University), entitled New Pew Data: More Americans are Getting News on Facebook and Twitter, also on July 14th to be an informative briefing on it. I will, well, try to sum up this summary, add some annotations and pose some questions.

First, for some initial perspective, on January 21, 2015, a Subway Fold Post entitled  The Transformation of News Distribution by Social Media Platforms in 2015, examined how the nature of news media was being dramatically impacted by social media. This new Pew Research Institute report focuses on the changing demographics of Facebook and Twitter users for news consumption.

This new study found that 63% of both Twitter and Facebook users are now getting their news from these leading social media platforms. As compared to a similar Pew survey in 2013, this is a 52% increase for Twitter and a 47% increase for Facebook. Of those following a live news event as it occurs, the split is more pronounced as 59% of Twitter users and 31% of Facebook users are engaged in viewing such coverage.

According to Amy Mitchell, one of the report’s authors and Pew’s Director of Journalism Research, each social media site “adapt to their role” and provide “unique features”. As well, they ways in which US users connect in different ways “have implications” for how they “learn about their world” and partake in their democracy.

In order enhance their growing commitment to live coverage, both sites have recently rolled out innovative new services. Twitter has a full-featured multimedia app called Project Lightening to facilitate following news in real-time. Facebook is likewise expanding its news operations with their recent announced of the launch of Instant Articles, a rapid news co-publishing app in cooperation with nine of the world’s leading news organizations.

Further parsing the survey’s demographic data for US adults generated the following findings:

  • Sources of News: 10% get their news on Twitter while 41% get their news on Facebook, with an overlap of 8% using both. This is also due to the fact that Facebook has a much larger user base than Twitter. Furthermore, while the total US user bases of both platforms currently remains steady, the percentages of those users therein seeking news on both is itself increasing.
  • Comparative Trends in Five Key Demographics: The very enlightening chart at the bottom of Page 2 of the report breaks down Twitter’s and Facebook’s percentages and percentage increases between 2013 and 2015 for gender, race, age, education level, and incomes.
  • Relative Importance of Platforms: These results are further qualified in that those surveyed reported that Americans still see both of these platforms overall as “secondary news sources” and “not a very important way” to stay current.
  • Age Groups: When age levels were added, this changes to nearly 50% of those between 18 and 35 years finding Twitter and Facebook to be “the most important” sources of news. Moving on to those over 35 years, the numbers declined to 34% of Facebook users and 31% of Twitter users responding that these platforms were among the “most important” news sources.
  • Content Types Sought and Engaged: Facebook users were more likely to click on political content than Twitter users to the extent of 32% to 25%, respectively. The revealing charts in the middle of Page 3 demonstrate that Twitter users see and pursue a wider variety of 11 key news topics. As well, the percentage tallies of gender differences by topic and by platform are also presented.

My own questions are as follows:

  • Might Twitter and Facebook benefit from additional cooperative ventures to further expand their comprehensiveness, target demographics, and enhanced data analytics for news categories by exploring additional projects with other organizations. For instance, and among many other possibilities, there are Dataminr who track and parse the entirety of the Twitterverse in real-time (as previously covered in these three Subway Fold posts); Quid who is tracking massive amount of online news (as previously covered in this Subway Fold post); and GDELT which is translating online news in real-time in 65 languages (as previously covered in this Subway Fold post).
  • What additional demographic categories would be helpful in future studies by Pew and other researchers as this market and its supporting technologies, particularly in an increasingly social and mobile web world, continue to evolve so quickly? For example, how might different online access speeds affect the distribution and audience segmentation of news distributed on social platforms?
  • Are these news consumption demographics limited only to Twitter and Facebook? For example, LinkedIn has gone to great lengths in the past few years to upgrade its content offerings. How might the results have differed if the Pew questionnaire had included LinkedIn and possibly others like Instagram?
  • How can this Pew study be used to improve the effectiveness of marketing and business development for news organizations for their sponsors, content strategist for their clients, and internal and external SEO professionals for their organizations?

Startup is Visualizing and Interpreting Massive Quantities of Daily Online News Content

"News", Image by Mars Hill Church Seattle

“News”, Image by Mars Hill Church Seattle

Just scratching the surface, some recent Subway Fold posts have focused upon sophisticated efforts, scaling from startups to established companies, that analyze and then try to capitalize upon big data trends in finance¹, sports² , cities³, health care* and law**. Now comes a new report on the work of another interesting startup in this sector called Quid. As reported in a most engaging story posted on VentureBeat.com on November 27, 2014 entitled Quid’s Article-analyzing App Can Tell You Many Things — Like Why You Lost the Senate Race in Iowa by Jordan Novet, they are gathering up, indexing and generating interpretive and insightful visualizations for their clients using data drawn from more than a million online articles each day from more than 50,000 sources.

I highly recommend a full read of this story for all of the fascinating details and accompanying screen captures from these apps. As well, I suggest visiting and exploring Quid’s site for a fuller sense of their products, capabilities and clients. I will briefly recap some of the key points from this story. Furthermore, this article provides a timely opportunity to more closely tie together seven related Subway Fold posts.

The first example provided in the story concerns the firm’s production of a rather striking graphic charting the turn in polling numbers for a Democratic candidate running for an open Senate seat in Iowa following a campaign visit from Hillary Clinton. When Senator Clinton spoke in the state in support of the Democratic candidate, she address women’s issue. Based upon Quid’s analysis of the media coverage of this, the visit seemed to have helped the Republican candidate, a woman, more then the Democratic candidate, a man.

Politics aside, Quid’s main objective is to become a leading software firm in supporting corporate strategy for its clients in markets sectors including, among others, technology, finance and government.

Quid’s systems works by scooping up its source materials online and then distilling out specific “people, places, industries and keywords”. In turn, all of the articles are compared and processed against a specific query. In turn, the software creates visualizations where “clusters” and “anomalies” can be further examined. The analytics also assess relative word counts, magnitudes of links shared on social media, and mentions in blog posts and tweets. (X-ref again to this November 22, 2014 post entitled Minting New Big Data Types and Analytics for Investors that covers, among other startups, one called Dataminr that also extensively analyzes trends in Twitter usage and content data.)

The sample screens here demonstrate the app’s deep levels of detail in analytics and visualization. As part of the company’s demo for the author, he provided a query about companies involved in “deep learning”.  (X-ref to this August 14, 2014 Subway Fold post entitled Spotify Enhances Playlist Recommendations Processing with “Deep Learning” Technology concerning how deep learning is being used to, well, tune up Spotify’s music recommendation system.) As this is an area the writer is familiar with, while he did not find any unexpected names in companies provided in the result, he still found this reassuring insofar as this confirmed that he was aware of all of the key participants in this field.

My follow up questions include:

  • Would it be to Quid’s and/or Dataminr’s advantage(s) to cross-license some of their technology and provide supporting expertise for applying and deploying it and, if so, how?
  • Would Quid’s visualizations benefit if they were ported to 3-D virtual environments such as Hyve-3d where users might be able to “walk” through the data? (X-ref to this August 28,2014 Subway Fold post entitled Hyve-3D: A New 3D Immersive and Collaborative Design System.) That is, does elevating these visualizations from 2-D to 3-D add to the accessibility, accuracy and analytics of the results being sought? Would this be a function or multiple functions of the industry, corporate strategy and granularity of the data sets?
  • What other marketplaces, sciences, professions and products might benefit from Quid’s, Dataminr’s and Hyve-3D’s approaches that have not even been considered yet?

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1.  See this November 22, 2014 post entitled Minting New Big Data Types and Analytics for Investors.

2.  See this October 31, 2014 post entitled New Data Analytics and Video Tools Affecting Defensive Strategies in the NFL and NBA.

3.  See this October 24, 2014 post entitled “I Quant NY” Blog Analyzes Public Data Sets Released by New York City.

*  See this October 3, 2014 post entitled New Startups, Hacks and Conferences Focused Upon Health Data and Analytics .

**  See this August 8, 2014 post entitled New Visualization Service for US Patent and Trademark Data .

Minting New Big Data Types and Analytics for Investors

Along with the exponential growth of big data in terms of its quantity, myriad of collection points, nearly limitless storage capabilities, and complex analytics, investors are keenly interested in discovering unique advantages from this phenomenon to be applied in the securities markets.* While financial institutions of all types have used sophisticated metrics and predictions to gain tactical advantages in their trading operations for many decades, burgeoning big data methodologies have recently created new opportunities for entrepreneurs to provide the financial services industry with ever more original and arcane forms of predictive analytics.

Investors now have data services available to them offering insights never previously feasible or even imaginable. Yesterday’s (November 21, 2014) edition of The Wall Street Journal carried a fascinating report highlighting three of these operations entitled Startups Tip Investors to Hidden Data Pearls by Bradley Hope. (A subscription to the WSJ Online is required for full access to this report on WSJ.com, but this piece was available here in slightly different version on CBS’s Marketwatch.com.) This additional extract page from the article is also available online and contains explanatory graphics of their formats and analyses.

How are these new data points being mined, examined and spun into forecasts? To briefly sum up the work of these startups covered in this article:

  • Orbital Insight analyzes satellite photos of building sites in 30 cities in China, cornfields, and parking lots in order to assess how their capacities might influence the markets in various ways. They are seeking to intuit “early indicators” of trends and influences. Their clients include hedge funds.
  • Dataminr sifts through a half a billion daily tweets in order to spot potential market moving trends ahead of the news services.** The link above to the graphics from the WSJ article contains a very effective infographic on this process.*** The company’s proprietary systems categorize and analyze all tweets in real time, discerns potentially useful patterns, and then distributes the results to their clients.
  • Premise Inc. uses a global system, now in 18 countries, that provides cell phone credits as payments to individuals who monitor the prices of various goods. From this input, the company tracks early inflation rates and other economic data. They believe that their data can differ from official government sources.

I recommend reading this story in full for all of its compelling details.

My follow up questions include:

  • Who watches these watchmen? Will market forces determine which of them are producing valid and actionable collection and analytics or should they somehow be subject to regulatory oversight?
  • Because these data types and analytics are so new, how are these companies and others like them addressing the distinctions between correlation and causation in their reports to their clients? Would it be beneficial for them to form a trade association to address this and other issues that might arise in the future for this nascent industry?
  • Are there entrepreneurial opportunities here for another type of new startups to vet the practices and products of such companies? That is, analysts who produce no new data types themselves, but rather, apply existing and, perhaps develop new, analytical tools for such assessments?
  • What other fields, markets and professions might benefit from this trend to discover and assess new data types in addition to finance?

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*    Please see this April 9, 2014 Subway Fold post entitled Roundup of Some Recent Books on Big Data, Analytics and Intelligent Systems.

**   Please see this July 31, 2014 Subway Fold post entitled New Analytical Twitter Traffic Report on US TV Shows During the 2013 – 2014 Season.

***  Please see this January 30, 2015 Subway Fold post entitled Timely Resources for Studying and Producing Infographics.