"Results are in: A cleaner webpage design equals more engaged readers"

Since we were just talking about design vs content, i thought this recent study was relevant: http://www.rjionline.org/blog/results-are-cleaner-webpage-design-equals-more-engaged-readers

term paper

As some of you'd already know, my research area revolves around transnational media culture in the era of globalization. For this term paper, I'll look at Western fans' reading and decoding of K-pop by conducting online ethnography and emplying several thoretical concepts from globalization paradigm. I'll look at how this contraflow of media culture challenges hegemony drvien by Western imperialism, and how it relates to racial/ethnic/gender identity.
While doing online ethnographic work, I found this funny video on YouTube.
https://youtu.be/sYgYNNBFBjU

Audience Psychology

Queen of social science

Assumption: People are ______.

A few theories related to audience --


Mere exposure:  https://www.youtube.com/watch?v=wEsC4gDkk-E

Facial feedback: https://www.youtube.com/watch?v=WR9lTqrkTYw
http://www.workplacehealthcare.co.uk/blog/wp-content/uploads/2014/01/pencil-happy.jpg

The Penny Gap: The chocolate experiment by Dan Ariely

Pricing and the perception of quality: The wine and energy drink experiments

Physical touch and the perception of quality


Pricing for physical, online, and hybrid products

Paywalls and Revenues

Pew's fact sheet suggests that in 2014 The New York Times and The Wall Street Journal had a similar number of users who paid for access to the websites (more than 700,000). The Boston Globe and Los Angeles Times also had a similar number of such users (around 60,000).  However, I am interested in examining the relationship between the type of paywall – metered or hard – and the growth in digital subscribes. The New York Times, for example, uses a metered paywall that allows online visitors to view a limited number of stories for free before requiring a subscription. In the first quarter of 2015 the outlet added around 33,000 net digital subscribers. Circulation revenue from digital-only subscription was 46.1 million – an increase of 14.4% from the first quarter of 2014. While this revenue accounts for about 20% of the total circulation revenue, it allows The New York Times to earn more revenue from paid circulation than from advertising. The newspaper has tried to improve in engaging users to the point where they are willing to pay – for example, by analyzing the last pages that users visited before signing up for a subscription. I have not seen similar data about The Wall Street Journal and other newspapers that use a hard paywall, but I am curious to see whether and how the number of their digital subscribers has grown, and look at their revenue from digital only subscriptions.

Free....dom

 Anderson and Ariely (I'll cite him like JC) both make great points of how "free" goods and services do wonders to our psyches. Ariely conducts a number of informal tests in his book Predictably Irrational, showing that peoples' tastes change dramatically when products go from free to $.01. An actual price becomes a barrier to purchasing that good. I think the increasing adoption of subscription services challenges this idea. Once people purchase a Netflix or Hulu subscription, they'll often put these subscription services on auto-pay. I wonder how much we begin assuming, over time, that the content we're getting from these services is effectively free. The value proposition is far greater than a cable subscription, at about $10 for thousands of shows and films. It's the cost of a single meal at Chipotle. I don't use Netflix nearly as much as most people I know, but I view it as a public utility rather than something I pay for. I wonder if "cheap" could start feeling more like "free?"


I used to agree with Clay Shirky's idea that we should give our content away for free in the short term in order to build a a fanbase. However, I think we (scholars, and well all of society), need to take a much closer look at the actual benefits to society of all of us creating more content. Sure, if I was to produce a tutorial video series, it could benefit Hyeri JUNG, but am I also creating a treasure trove of data for advertisers to learn more about me? And in a sense, give them more opportunities to target me, and take my disposable income with purchases I perhaps do not need? Also, I think we need to be weary of the fact that, much of the power we as consumer-producers have to distribute our free content rests in the hands of a few large corporations. I am not saying everything is inherently bad or good, but free comes with a lot of considerations. I think society needs to better understand how much free stuff takes away from our freedom.

The psychology of digital media audiences—willingness to pay

The Psychology of Free by Anderson (2009) gave an interesting perspective on free subscription. I had subconsciously thought about this but never in such ways as the penny gap and the cost of zero cost. It makes logical sense to think that a source that has always been free remain unaffected if it continues to remain free to consumers. However,  that paid content is devalued and discredited when it becomes free also makes logical sense. Some equate money with more credibility, higher quality, more information, etc. However, if the same content becomes available for free, its consumers may question whether or not it will have the same amount of credibility, quality and information as it did previously when it was paid. This made perfect sense to me. However, this paper was published in 2009. I wonder if this will still be true today in 2015.

In 2015, I believe Dan Ariely's version of "free" is true as was described in Predictably Irrational. He says "zero is an emotional hot button - a source of irrational excitement." Free does not speak to price/quality as it did previously but rather removes risk and we "forget about the downside." In this case, I think the penny gap works to rationalize consumers preference for free things over paid. Now, we see more free content change from free to paid which is disgruntling to a lot of consumers. Some consumers only look for free content and disregard any paid source because the quality of information is thought to be similar if not the same. Paid content is often called a premium now: the free version gives you everything you need but paid accounts give you more options. Therefore, finding anything for free is exciting and preferred.

"Information wants to be free. Information also wants to be expensive … That tension will not go away." (Anderson 2008)

I've paid a lot of attention over the last several years to the model of online news, hoping that a clear leader in monetization would emerge in the same way that the iTunes store brought change in how most obtained their digital music. The chapter slightly depressed me because it started out strong by comparing the two newspapers but then failed to really address how the rest of the chapter could apply to news. Other than implying "if you started free you're good. if you didn't start free, sucks to be you." The author did a lot to talk about the gap between free and charging ANYthing, which i found very interesting. I never thought of the Penny Gap that way before, and how even a financial commitment of $0.01 is enough to change someone's mindset.

Comparing the chapter to the paywall data, it appears as if most people are still willing to "pay themselves less than minimum wage" to find their content elsewhere. The New York Times and Wall Street Journal have a M-F average circulation of more than 2 million yet only ~700,000 behind the  paywall. I'd love to see a comparison of how many metered clicks the NY Times gets versus paid clicks. What's the falloff after the 10 free monthly views are reached? Do people pay or do they just open it in another browser or in an incognito browser to trick the metering code? 

----

In a funny turn, i just opened an email and this ad was at the bottom:

Online or print?

The psychology of digital media audiences—the lack of engagement; the perceived inferiority of digital content

In Chyi (2015), the idea of digital natives are discussed. Even though many think that the future is online due to the rise in the number of digital natives, many still prefer print newspapers. The stats provided in the paper support what I wrote about last week. I argued that print newspaper will still be consumed by older generations mainly due to habit and being a laggard in the technology adoption model. The baby boomers, are aging and this large group of people are non-digital natives. Chyi (2015) offers that 46% of people aged 65+ report reading print newspaper yesterday while only 7% of those aged 18-24 read print newspaper. Baby boomers make up the largest group of people in the United States (until this year - Millennials will outnumber them). They will support the use of print newspaper even if digital natives do not. 

It was also surprising to find the preference for print over online newspapers for college newspapers. This may be attributed to availability, on-campus promotion, entertainment as well as it being offered for free. Since younger generations do not find newspapers enjoyable or entertaining, as the paper stated, they will not intentionally go to an online college newspaper site but would be open to consuming printed newspapers that are handed out on campus.

The point of online advertising revenue is interesting. Especially, mobile advertising is something that has been growing vastly in the last 5 years. I was surprised to hear how low the revenue off of web ads and mobile ads were (1% of total revenue). If paywall exists, then perhaps it would benefit mobile newspapers to get rid of advertising all together to increase audience engagement.

News: Nielsen's Total Audience Measurement

http://www.adweek.com/news/television/first-look-nielsen-s-total-audience-measurement-and-how-it-will-change-industry-167661

Multiplatform audience measurement

Displacement effect --

• Is it important? 
• Different approaches
• Medium-centric (The more time spent on medium A, the less time spent on medium B)
• User-centric (The more time spent on medium A to fulfill some needs, the more time spent on functionally similar media)
• How to measure it?
• Direct question (Are you spending more or less time on medium A because of medium B?)
• Absolute time (w/o considering overall media budget)
• Relative time
• Likert Scale (e.g., frequency of use)
• Longitudinal data (e.g., Brown sisters -- 40 years of photo data and how the NYT promoted the story on social media)
• How different measures lead to different conclusions?
• Exercise: Mobile news

Multiplatform news consumption --

• Getting complicated (see Niesen's new approach to multiplatform TV consumption)
• The repertoire approach (TV channels, Websites, cross-media media use for specific needs)

Beyond use, what other variables are of interest?

If you build it, they may not come

After reading this week's articles, I couldn't help but wonder why the legacy newspapers don't listen to their customer bases more? As Chyi (2015) points out, "if digital natives are prone to news in digital formats, they should have dropped the print edition of their campus newspaper by 2011. However, results collected through a national survey of nearly 200 U.S. college newspaper advisers indicated that the print edition reached nearly twice as many readers as the Web edition on a given day." I think this example is indicative of newspapers not learning from their loyal readership.

On the other hand the digital native publications don't have to play by the same rules. They don't have long-standing guilds (though some now have unions) to factor in to their plans or print operations. They were made specifically for the web and have more or less mastered the art of making money on digital content: Make a lot of stuff, make it cheap, and blur the lines between your content and the advertisements. And of course, make sense of your analytics, more specifically what your customers are demanding.

Digital Natives and News Consumption

I was particularly interested to read about "digital natives" and newspaper consumption. Contrary to popular belief, Chyi (2015) showed that "digital natives" do not necessarily prefer online newspapers to their print editions. For example, a national survey of US college newspaper advisers indicated that their students overwhelmingly preferred to read the print edition of the college newspaper. Other studies found that while older people were more likely than younger ones to read a print newspaper, the penetration of a given print newspaper was higher than its online penetration even among young people. That said, whether the news is in a digital or print format, young people are less likely to find news interesting or relevant for them (Lee & Chyi, 2014). They prefer entertainment over news. Chyi encourages newspaper managers to acknowledge that print is their asset and digital is not their forte. I tend to agree, but I am also concerned that in an attempt to attract more young readers to news websites, these websites may display even less news and more content related to entertainment and non-public affairs.

Readings for 10/19/15

I really enjoyed reading this week's articles. Whereas Dr. Chyi’s study indicates that digital news is not replacing newspapers yet, Lee and Leung’s study indicates that the Internet is displacing all traditional media (i.e. newspapers, television, radio, and magazines) rather than supplementing them. According to Lee and Leung’s study, Internet users for news/information and entertainment do not spend more time with functionally similar traditional media for the same purpose. Their findings reject the “more-more” hypothesis as suggested by the user-centric approach. The Internet is shown to have an overall displacement effect, and not a single instance of a “more-more” situation occurred in the use of traditional media among Internet users. The ‘‘more-less’’ scenario is seen across all traditional media.
In contrary to Lee and Leung’s study, Yuan’s study supported the user-centric model. She looked at the old and new media for the news audience from the repertoire-oriented approach, hoping to provide more fluid and dynamic analysis than the dichotomous distinction between the substitutive and supplementary relationships. Her focus on media repertoire was particularly interesting as I see it personally applying to me: for example, setting favorites on my Internet. I think the repertoire-oriented approach will greatly depend on how much effort I have to put into when using new channels. Her study only focused on news. I wonder if it would be the same for other media content, such as entertainment.

Some might argue that whether or not new media replace or supplement old media would depend on multiple factors, given they provide similar functions. However, how can different media serve the same needs and functions? As Marshall McLuhan once said, “Medium is the message,” different media platforms provide distinct senses and require audiences' physiology to function differently. Although the media content might be the same, audiences change their attitude when interacting with different media platforms.
The amount of using the Internet will vary in each society depending on how developed and fast the Internet is. For example, Korea is the most wired country in the world according to the world statistics. Basically, there’s free high speed wi-fi wherever you go including subways. Because of this, there are many online-related burgeoning industries (and big conglomerates of course) coming up with innovative ways of delivering content via unprecedented media platforms. For example, there’s a new form of media called “web drama,” which is relatively very short (about 10 minutes per each episode) compared to the traditional drama shown on an old medium, television. This is increasingly getting popular in the K-drama industry mainly because about 99% of all young Korean people have a smartphone with web streaming capabilities.

Multiplatform audience measurement; displacement effects; the repertoire approach

In Lee and Leung's (2008) article, the displacement effects of the internet is discussed in multiple aspects: mediumcentric, usercentric, more-more, and relative proportion of time spent on media. Although I can see how displacement effects can occur depending on the variety of medium offered or users' preferences/media consumption behavior, and the amount of time spent on a specific medium; I cannot see how the authors could have hypothesized "more-more" as opposed to "more-less". This goes against their next hypothesis of time as a relative proportion out of a total number of time spent on all mediums. For example the second hypothesis reads:

          H2: The more time Internet users spend on news and information, the more time           they spend on the functionally similar traditional media (e.g., newspapers, radio,             and magazine) for their news and information needs.

However, according to relative proportion vs. absolute time spent on media, one would see that "relatively," if one were to spend more time on a single medium, then he/she would be less capable of spending time on another medium. A "more-more" would never result in a displacement effect, thus should not be used to measure displacement effects. This is also supported by medium-centric approach.

I liked the idea of both medium and user-centric approaches but leaned more towards the latter. New mediums will always arise but it's acceptance and use will always depend on the user. I believe that substitution is solely dependent on the users. Furthermore, since mediums have different purposes and satisfy different needs, I believe supplement will occur more than substitution.

Also, Yuan (2012) suggests media convergence is due to the change in the news media landscape. However, I think that another large role that attributed to the move to digital and mobile media technology is the push for green initiatives and pro-environmentalism. In conjunction with the advancements in technology, environmentalists realized that everything can be moved into the digital space, reducing the need to further harm the environment. The 21st century is a time where everything is eco-friendly, green and organic. With such times, things such as digital receipts, paper-less billings, re-usable canvas grocery bags, etc. have emerged. Likewise, newspaper has gone digital also. If this hadn't happened and news content only stayed on paper, I think substitution of newspaper could have been avoided. However, now that the same content is available more conveniently and in a more environmentally friendly form, there is no need to linger onto folds and folds of paper anymore. However, the greying baby boomers, who make up a large portion of the population, may continue to support printed newspapers out of habit.

Movie time!

The "real" audience-driven model of content creation through "big data":
2010 ISOJ Keynote speech (Demand Media): https://vimeo.com/20938653 (Start from 16:17)
-- They're recruiting freelancers.

On "built-in obsolescence" and "slow news": Justin Lewis featured in Consumerism & the Limits to Imagination (Start from 21:10 Bubka Principle)

Audience measurement on mobile and social media

I liked Barthel and Shearer's (2015) Pew article on Twitter. How Americans use Twitter for news is discussed but the in-depth exploration into how people use twitter, what they are talking about, who they follow, etc. can be applied to many other disciplines. For example, I took an in-depth look into the topics of conversation through data mining of tweets in a emergency health communication context. I would also like to do textual analysis of tweets in an advertising context in the future because I believe a study of consumer behavior is important. Studying consumer behavior has been limited and rather abstract in the past but advancements in technology allow us to actually observe and learn from real data.

The article mentioned a disappointing aspect of their twitter examination. The sample size yielded was too small. This is a challenge I encountered when I first started poking around with data mining. If there is not a controversy or crisis that is a top topic of discussion, it is unlikely that researchers will find enough data around a specific topic. If a researcher is interested in a particular aspect of advertising, let's say privacy, he/she will not find thousands of comments being generated about audience's privacy rights because it is a general problem and not a current hot topic. On the other hand, researchers can expect to get a lot of data around events such as the Ebola outbreak. Therefore, I agree with Barthel and Shearer that Twitter analysis can be disappointing and limiting. However, compared to the days prior to social media, Twitter allows researchers to observe consumer behavior in real-time which is valuable and extremely useful if used in the right way.

Fun Reading about Social Media

I enjoyed reading the four items, as they present interesting findings regarding the use of Facebook and Twitter. While I already knew that less than a quarter of adult Internet users in the US use Twitter compared with more than 70% who use Facebook, I was somewhat surprised to see that Twitter users are also less active than Facebook users. Only 38% of Twitter users use it daily, compared with 70% of Facebook users. Also, 40% of Twitter users do not even use it once a week. As for the other Pew's report: It was interesting to see that among Twitter users who tweeted about news, less than 20% focused on government and politics news. However, given the small sample used for this study, I would treat the findings with caution. The sampling error may be very high. I like the typology offered by Bruns and Stieglitz (2012), but in addition to thematic and contextual factors that influence the usage of different communicative tools on Twitter, I would like to see how different types of users are associated with the usage of these tools. For example: it is plausible that journalists, politicians, and "ordinary" citizens use these tools differently even if the topics and events are identical. Finally, as for the experiment pertaining to Facebook: while the number of likes did not influence the way in which users evaluated a news story, I am curious to see if the new "dislike" button will have an impact. Also, soon we may be able to examine the influence of new emoji buttons on Facebook. Check out this.  

Twitter, huh

I think the biggest thing that stood out this week was how not only is Facebook and Twitter use plateauing, but how only 23% of adults say they are on Twitter! It was a good reminder that you can't just generalize based on experience because i would not have guessed that few people were using Twitter. It also got me wondering why so much interest seems to be focused on the service when it doesn't have as much of a user base as ANY of the other services surveyed. Possibly because of how open it is? It's a lot easier to scrape data from Twitter than any other social network where users can close down their data. I like what Krishnan brought up about the actual content of the news tweets, as many of them could possibly just be tweeting the same story over and over during the day to drum up interest (and Twitter users are encouraged to do this).

Finally a guide to study Twitter!

I really appreciated Bruns & Stieglitz's "how to" guide in studying Twitter. I often struggle to think of Twitter as a text or a space I want to study, b/c I'm not sure where to begin my research questions. However, this piece was exactly what I needed! Ok now for more scholarly notes.

1.) It was really interesting to see how few people within the Pew study were on Twitter, and even a smaller percentage were active on Twitter. Additionally I found it very interesting that, "tweets from news media make up a significant portion of a user's feed." I'm curious to know if this deluge of content in the form of story promotion, retweeting stories, up cycling old content from news organizations devalues their social currency with audiences. In a democratized space such as Twitter at what point do audiences see the news as noise?

2.) Finally, if you haven't all read the New York Time's 12 page memo, Our Path Forward, I suggest you take a look. They've hit 1 million paying digital subscribers and are sharing their plan forward. This line stuck out to me and I believe it is very relevant to our class: "Young readers were the first to shift to mobile and the first to embrace social platforms, and they have become reliable first indicators of major trends that ultimately affect our entire audience."

Interactive nature of social networking site (SNS)

I found Winter et al.’s study quite interesting. As the authors stated, the design of Facebook and similar SNS put a large emphasis on user reactions, fostering a convergence of and high interaction between mass and interpersonal communication. Then, does this mean that we are having a larger number of opinion leaders than the past since more people have more access and means to post and share their opinions online? Would opinion leaders be discouraged if their opinions posted online are severely opposed by other users’ comments or low numbers of likes (negative peer reactions)? Can we still set the same standard of opinion leaders? Revisiting the theoretical concept of opinion leaders, which date back to the study by Katz and his colleagues, in a new media sphere would be interesting to study.

Google Trends

Thought this was interesting: Searches for University of Texas football, off to a pretty horrible start, is currently at 95, second only to when they won the Rose Bowl in January 2006. The annual peaks in September can be expected as that's when college football season kicks off, but I thought it was interesting to see how heavily it's being currently talked about. PAST that, look at what happens when you strip out international audiences and focus only on the U.S.

The role of big data in the research process

The end of theory? http://archive.wired.com/science/discoveries/magazine/16-07/pb_theory 

How big data make a difference in these two processes?  

A reminder from "Overcoming physics envy":
"The analysis of empirical data can be valuable even in the absence of a grand theoretical model."

"Social scientists can no longer do research on their own" (Gonzales-Bailon, 2013). 

Social Science in the Age of Big Data

While big data provide us new opportunities to make sense of the world, they do not "speak for themselves" and do not override the need for theoretical models. Contrary to Anderson's argument, theory still matters as it "can help discriminate noise from signal, and provide the right context for the interpretation" (Gonzalez-Bailon, 2013, p. 9). For example, we can use big data to identify key actors and flows of information in digital networks, but based on the findings we may construct different stories. In order to make sense of the findings, we need to use theoretical lenses. 

It was also interesting to read that even though there are different types of actors in networked spaces, the public opinion is still overwhelmingly shaped by a minority of actors. For example, as Gonzalez-Bailon mentioned, Wu et al. (2011) found that about 0.05 percent of Twitter users account for almost half of public opinion. However, I suspect that it may vary in different contexts. Therefore, I think that further research is needed.

Readings: Big Data

I read the Wired article first and got really depressed until I got to the Gonzales paper. I tend to agree with Gonzales more on Big Data, in that yes data tracking supersedes some of our previous methods but that it is still important to have human researchers that can provide context. In particular:

Only when the data are assembled in the right way, by focusing on the signal and disregarding the noise, can we build a story that makes substantive sense.

Anderson doesn't agree for the necessity of theory and models to help explain data but I still side with Gonzales that there needs to be a subjective approach for us to truly understand the data especially when it comes to Social Science. Even in the hard sciences, I don't understand Anderson's example of the research "discovering" new species that he knows nothing about. What is the point of having "a statistical blip - A unique sequence that, being unlike any other sequence in the database, must represent a new species?" I would imagine one would need to know the details and not just be satisfied with a vague statistic that there probably is something. Did we send a rover to Mars to look for water or were we satisfied with the statistical probability that there would be water? It may not be completely related, but I saw an interesting article last week about the use of "small data," mainly in content-based apps like Netflix and Tinder. The author talks about how we can't handle large deluges of data and instead respond better to "card-based" types of apps where we're presented with a series of simple information.

Big Data and Audience Research

I agree with Chris Anderson's oversimplified argument that big data has ushered the end of the scientific method. Though I imagine in the 7 years following his blog post his position has possibly evolved, his article assumes that big data is simply available at our disposal. However, "big data," especially related to to audiences is not easily at the hands of researchers. It sits behind the gated server walls of Facebook, it needs to be munged and reorganized, and there is a relatively high level of human capital that goes in to analyzing this big data. The other thing to note about audience data is that natural language processing of peoples' conversations, social media postings, etc., is still relatively crude. Machine language and NLP have come a long way but in order to get high level results isn't a cake walk.

I think having access to large datasets is excellent, but as Jen Schradie of UC Berkeley wrote in this blog post , "I am not suggesting fishing expeditions in lieu of hypothesis testing nor any Anderson-esque junking of the scientific method. The numbers do not speak for themselves. Instead, it is our job as social scientists to understand the difference between the data, whatever its size, and the method, whatever that may be." I agree with Schradie. We can't simply starting poking at the data. Understanding a group of people, a place, a set of ideas and having some type of expertise is essential otherwise we lack the cultural capital to really engage the "big data" we've collected. 

Audience measurement with big data; Google Trends

I don't think the availability of big data will put an end to the need for theory. They actually go hand-in-hand as big data is meaningless without interpretation of what it means. As Gonzales (2013) mentions in his article, "disentangling signal from noise is still a subjective matter, as is providing the context that will help identify meaningful correlations and discard those that are unsubstantial." An abundance of data is good but an abundance of meaningless data is not. Therefore, the WIRED article by Chris Anderson claiming that Google conquered advertising simply with mathematics is disputable. It may be true that Google analytic offers a tool to analyze big data but to assume that this was done without any knowledge of the "culture and convention of advertising" is completely wrong. As we learned in class, we have to know what keywords to look for to make an effective campaign. Also, looking only at SEO disregards integrated marketing strategies and would do no more than get exposure. Furthermore, without knowledge of consumers and what they want, how can we arrive on a successful keyword/campaign that matters? I think that Google has provided a better way to determine the performance of campaigns, not how to actually run them.

Social media's impact on television watching

Hi all -

Here's an article by Farhad Manjoo in the NYT about social media and tv that I thought was relevant to this class:

http://www.nytimes.com/2015/10/05/business/media/social-media-takes-television-back-in-time.html

-Krishnan

Is data the new oil in the information era?

The article by Anderson extols Google’s analytical tools and how successful they have been in making profits for the company. I find this approach of big data/Petabytes analysis, such as Netflix’s Cinematch, quite useful for audience research. But wouldn’t it polarize the audience? We can argue indefinitely whether this mathematical/defying-traditional-scientific-method approach is right or wrong because it’s an epistemological matter.
Google’s founding philosophy is that they don’t care about human beings and their causal relationships, cultures, contexts, behaviors, motivations, etc.; all they care about is correlation. If the statistics of incoming links say this page is better than that page, then that’s good enough. This might be useful for commercial business people or economists. The epistemological problem here is that studying about social science, human being and communication doesn’t work like that.
Attempting to know about human beings and their driving motivations, cultures and contexts matter because of the importance of accuracy and outliers. For example, Google translator is anything but useful. Google translator uses an enormous amount of data and textual references to translate one language to another. But if two languages are radically different in terms of grammar, structure and nuances, it gets so inaccurate to an extent that it’s just not useful at all. Also, sometimes it’s the outlier that really matters in the realms of society and communication.
The article contends that we don’t need to know why people do what they do as long as they do it. It seems to criticize traditional inductive scientific reasoning, but it neglects to mention the probability of deductive reasoning. I’m not sure whether the author is equating statistical algorithms to interpretations of qualitative researchers when they analyze their corpus of research data.

Most millennials are willing to pay for content, but not so much for news

http://www.niemanlab.org/2015/09/most-millennials-are-willing-to-pay-for-content-but-not-so-much-for-news/

More millennials say they’ve paid for print magazines (21 percent) and newspapers (15 percent) than digital magazines (11 percent) and online newspaper content (10 percent).