essay helper
SHARE THIS PAGE

Blog

categories

tags

RECENT FEATURED POSTS

Event recap: Journalism Educator’s Symposium

On Wednesday, Sept. 21, 2016, the Tow Center for Digital Journalism at Columbia Journalism School hosted its inaugural Educator’s Symposium. The purpose of this event was to bring together journalism instructors and practitioners to share challenges and best practices for journalism education in today’s rapidly changing reporting and publishing environment.

Below is an overview of the day’s three primary sessions, each of which consisted of two lightning talks followed by group discussion. The three themes were selected from among those that participants selected through an open invitation to present.

Session 1: Data, design and visual journalism

Our first session featured two talks focused on how to keep data journalism courses both rigorous and engaging. In the first talk, Professor John Wihbey addressed why giving students “messy” data sets was important (even if might seem frustrating at first), while Professor Meredith Broussard shared key strategies for keeping long and sometimes complicated class sessions active and engaging.

1.1 Messy Datasets in the Classroom – John Wihbey / Northeastern

Session slides

 

Make it personal

Students can be uncritical of both data and tools when they are “too clean.” One way to help students really think about where data comes from and what it represents is to have them do data collection & deal with it by hand.

Example exercises:

  • Have students create a “data diary” by recording data about their lives for two weeks and using it as a source.
  • Students decided to track their personal curses, which forced them to think about how to organize data without using pre-gathered data. They were also limited to hand-drawn presentations.
  • Go around campus, observe people by demographics and come back to class and map that data.

Get in groups

The value of group work came up in both sessions, in part because it cuts down on the need for “tech support” for individual students. It can also make it easier to give feedback in large classes where, if time is tight, you can both get to everyone and help students prioritize by identifying three things that worked and three things that didn’t in a given project/presentation. Finally, group work is a great place to have students talk with one another about obstacles: It often feels easier to solve someone else’s reporting problem.

Discuss mistakes

While there are a lot of great examples of data journalism out there, there are some cautionary tales as well. Providing students with examples where things have gone wrong (or asking them to do the same) can highlight pitfalls of the practice. Also, having them reflect on their work to determine where they themselves have gone astray offers both important insight and an added learning opportunity.

Don’t forget the story

Unsurprisingly, a focus on story permeated our discussion. Data “cleaning” exercises shouldn’t be undertaken for their own sake, but always embedded in a reporting and story development process. This is another area where working with “messy” data aids the journalistic side of the learning process: Messy data is intriguing, and forces students to dig into the details and context in order to make sense of it. And since most real-world data is messy, it helps reinforce the fact that the kind of data we use in journalism is about real things that happen in the world, rather than a collection of abstract measurements. This can help students develop the emotional and narrative aspects of a data-driven story, driving attention to the all important question: “Why do I care?”

1. 2 Planning Interactive Tech Classes Where No One Falls Asleep – Meredith Broussard / NYU

Session slides

Tech-focused classes can be exciting for students eager to gain job skills, but often this means wading through long class sessions and lots of jargon. To keep students attention (and keep it on the right things) careful planning can keep things moving while still leaving room for spontaneity.

Have a plan

  • Share your syllabus: A well-developed plan for the overall course helps students plan their time and understand what they can expect week-to-week.
  • Even during a given class session, putting lesson plan on the board to helps involve students in the progress of the lesson and reinforce classroom direction.
  • Pop-up projects: Perhaps counter-intuitively, having a plan can actually allow greater flexibility, because it’s easier to see what will have to be cut or reconsidered. This lets you run a pop-up newsroom during important events, for example, even though it is not in the syllabus.

Mix it up!

  • Choreograph your class, keeping in mind that you generally want to balance four key elements, especially during long class sessions: guest speakers, lectures, peer-to-peer learning, and presentations or critiques.  
  • Students can be intimidated. Mixing something familiar with something scary: a cool new app + People On Street interviews
  • How do you re-write this story? Take some good stories, break into constituent parts. How would you storyboard it for a different platform?

Game on

  • How to make quizzes that students look forward to? Technique is to have ungraded quizzes administered at beginning of class, fun questions. Ungraded means low stakes.
  • Role play: Each person becomes a part of the web networking system
  • Physical exercises: combining apps with physical networks

What’s needed:

  • Example datasets that are good for teaching

Session 2: Social Media, chat applications, community engagement

In the second session, we took on Snapchat and social journalism — two areas of practice that were relatively new to many attendees. Both topics also instigated important discussions, from Snapchat’s imperative towards vertical video to the relationship between social journalism and advocacy — a topic that would return to the forefront in our third and final session.

2.1 Snapchat for Journalists – Sissel McCarthy / Hunter College

Session slides

Although Snapchat is used heavily for personal messaging, in January 2015 the platform launched “Discover” with a handful of publishers. Yet many questions remain about how to use the service effectively for journalistic content.

Why it appeals

The appeal of Snapchat comes largely from its popularity among millennials (it is the 8th most popular download), who value the fact that it is not a “sterile, adult” space.

Approaches to teaching

Snapchat offers a unique opportunity to observe and critique how news organizations are leveraging a new format, and it also provides students with room to experiment with how a story looks across different platforms. Although not all instructors have deep experience with Snapchat, it does offer the chance for students and instructors to learn together.

Example exercises:

  • A Snapchat explainer as a story.
  • Using Snapchat to do six words on race, student loans etc. (adapted from a Wall Street Journal piece)
  • Tell me a story about yourself or show me a process using Vine or Snapchat.

Snapchat can also be a good segue into other types of short-form visual content, like animated GIFs, “snackable graphics”, Twitter cards, etc. These kinds of materials can often be used across several platforms, including Snapchat.

What’s needed:

  • A better understanding of Discover. Is it really a platform where people are consuming news? Most statistics cover Snapchat as a whole, and more specific metrics can be difficult to obtain.

Resources

 

2.2 Teaching Social + Community Journalism – Carrie Brown / CUNY

Session slides

The Social Journalism program at CUNY takes a service- and audience-oriented approach to journalism, and students there focus on reporting about specific communities. While our discussions touched on a range of issues—including advocacy, business models and engagement—the program’s mix of reporting, technology, data and engagement certainly reflects the direction of many journalism programs today.

Journalism, ethics, advocacy

If social journalism places communities and audience interests at the center, does this make it a type of advocacy, and if so, what are the drawbacks? In the past, some journalistic projects driven by  audience suggestions ended up with a focus on watchdog journalism, which is at least some part of many organizations’ mission. If a story gets a strong audience response, is it an artificial line for journalistic organizations to say: “We are going to hide information about how to help the subject of the story” because that would be advocacy?

Ethical considerations around social journalism also work in the other direction, however. For example, is it a problem to cater to your audience’s interests just to increase revenues. And if so, how is this different from the practices of “niche” publication? It also raises questions about the boundaries between engagement and selling a product – even if that product is your own journalism.

Student perspectives

In general, instructors’ experience was that students enjoyed being  followed, and having their work liked  and shared. Many instructors also reported that their students in journalism programs were often quite open about their interest in working for advocacy and aid organizations. In any case, one of the most important lessons of social journalism—and perhaps all journalism—is about how to work with and understand communities that students are covering, not just “parachuting in” when a particular story breaks.

Editorial perspectives

In the 1990s there was a fair amount of resistance among elite news organizations to the perceived loss of editorial control that can result when audience interests take the lead. Yet given that the “one-to-many” relationship between news outlets and audience members no longer as applicable, it may be time for journalistic organizations to explore their role as sources of research, alternatives and solutions for problems of public concern.

Resources

Session 3: Emerging Issues in Digital Journalism

Our final session of the day focused on emerging issues in digital journalism, and in doing so returned to some of the emerging themes of the day: ethics, advocacy and new platforms for journalism. The first talk of the session prompted reflections about how to encourage creativity and innovation, without losing sight of the essentials. In the second talk, recurring questions around advocacy and ethics led to some spirited exchanges about traditional journalistic assumptions and the evolution of the field.

3.1 Writing for the End User – Aileen Gallagher / Syracuse University

Session slides

Emerging issues

Journalism is still a rapidly evolving field, which means the job market is evolving as well. While we all want to prepare our students to be competitive, it’s important not tie yourself  to teaching based on what job a student might get. Instead, make sure they learn the basics and encourage students to think about what kind of job they want to get in two years.

For instructors, of course, it can be difficult to always stay ahead of the curve, especially with the pace of change in technologies and platforms used for journalism. One thing to keep in mind is that this presenta an opportunity for co-learning with your students (see Snapchat discussion, above). But you can also focus on the role and function of story on new platforms, whether or not you understand the technology intimately. Differences among platforms also highlight the need to have an “elevator pitch” for every story or project, so that those who don’t know the tech (which may include future employers) can appreciate the piece. Because, as one participant pointed out “if the story is missing, the[se projects] are no good to us.”

Innovation

Perhaps unexpectedly, one key strategy for innovation encouraged by the those teaching tech-heavy courses was to remove technology from the equation. Paper prototyping, storyboarding and discussion exercises were all advocated as a way to encourage critical thinking and creativity, rather than having students “think through the tool” when they go straight to a particular program or format.

Example exercises:

  • Teach how students how to learn by modeling an approach for exploring a single tool, then having them follow this process for a new one.
  • Leverage the value of user research: Have students observe audiences and how they actually use and engage with various forms of journalism.
  • Bring in storytellers. Students are often inspired by individuals, so have those folks come and talk about their work, whom they are reaching and how.

3.2 Making the Case for New Taxonomies in Journalism – Jan Schaffer / J-Lab

Session slides

Our final talk of the day focused on changing attitudes about journalism — from both inside and outside the industry. Pulling together conversations from earlier in the day, the discussion focused issues about advocacy, audience, and the role of journalists and journalism in a social-networked world.

Advocacy, independence and investigations

A key question that once again took the floor during this session was: What constitutes advocacy? Does publishing a visualization of how a downtown thoroughfare might look count as “activist” or “advocacy” journalism? (A little extra: See this Sept. 30 interactive from The Times). What about journalism that encourages participation in civic programs, like pre-kindergarten and voter registration? If these activities do constitute advocacy, how does this connect or conflict with other newsroom values? As one participant pointed out, investigative journalism often has an “advocacy” element, yet investigations are considered one of the most important types of even “traditional” journalism.

Audience interest

The question of audience interest also came up in this discussion, with audience behaviors suggesting that journalists’ news priorities do not carry the weight that they once might have.  In many cases, audiences are paying increasing attention to the work of advocacy groups because they are invested in the issues they cover.

Social Media

Social media has also changed the role of journalism in society, as the basics of breaking news increasingly reach audiences via non-news platforms and feeds. As former Columbia Journalism School Dean Nick Lemann has highlighted, this necessitates a shift from the “hunter-gatherer” model of journalism to a “value-added” model.

Not Everything is Neutral

With issues of false equivalence dominating the media’s self-reflections this election cycle (including this piece from almost exactly one year ago), the idea of “neutrality” about certain issues arose as well. One participant asked whether there were certain issues about which one could (or should) claim to be neutral. It was also highlighted that certain forms of journalism have always been an exception to the traditional rule of “balance.” As one participant noted, journalism about 9/11 is not expected to represent “both sides.”


The Tow Center’s Educators’ Symposium was just the first in a series of gatherings that designed to provide journalism educators with opportunities to discuss their work and share their insights. If you have questions, comments, or contributions, please feel free to email us at towcenter@columbia.edu with the subject line “Educator’s Symposium.”

Tow Takeways: Journalism & Design

Heather Chaplin, the author of our latest report on journalism and design moderated a panel Sept. 21 with design leaders from NPR and The New York Times. Here are key takeaways in tweets:

screen-shot-2016-09-27-at-2-36-36-pm

READ MORE

Changing Course: From Wall Street to Pulitzer Hall

Editor’s Note: This blog post kicks off a new Tow Center series of student and faculty stories on the intersection between journalism and technology. If you are interested in contributing, please contact n.fernando@columbia.edu

By Jose Manuel Villa

manuel

I have had two major existential crises in my life:

READ MORE

“The next tweet could get you fired!” – Or promoted?

a-ingram-cc-by-nc-nd-2-0_cropped

Note: This is the third post in the Beyond 140 characters series, which investigates how, why, and under what circumstances political journalists engage with Twitter. This piece shares some of the project’s key findings. The previous post reflected on methodology and data collection.

READ MORE

Platforms and Publishers

The Tow Center is embarking on a multi-year project researching the relationship between journalism and social platforms in order to promote mutual understanding and best practices for conducting journalism on the social web.

In the rush to capture audiences and establish new commercial businesses the impact on the citizen rather than consumer is often overlooked. Non-commercial functions of the free press, such as defending free speech, protecting vulnerable sources, resisting government pressure for censorship, practicing commercial transparency, are new requirements for technology companies.

This project will provide news publishers and journalists with a more granular understanding of how journalism and independent publishing is affected by integration with social media, and how platform companies are also having to adapt to a new role. It will engage software and social platform companies in understanding best practices for supporting ethical journalism in a new environment.

We’d love to hear from you if you are grappling with the issues of publishing and monetizing distributed content. Please get in touch with Nushin Rashidian: nr2343 [at] columbia [dot] edu.

To learn more about the project, read Emily Bell’s “Who owns the news?” on Columbia Journalism Review and read about our preliminary findings.

Follow the Leaders? Jill Stein and Gary Johnson’s Twitter and Facebook Activity

Last week, the Green Party held its convention in Houston, Texas to nominate Jill Stein as its candidate for president. The other major third party in the U.S., the Libertarian Party, chose Gary Johnson as its candidate back in May. While no third-party candidate has ever been elected president, the record high disapproval ratings for the two major party candidates (Hillary Clinton for the Democrats and Donald Trump for the Republicans) suggest that voters may pay more attention to third-party candidates than they have in the past.

Third parties play a strange role in American politics. Some scholars have observed that the structure of the American political system makes it hard for third-party candidates to win, in large part because of the winner-take-all elections. Third parties in America more often act as pressure groups. For example, Ross Perot, the Reform Party presidential candidate in 1992, brought the issue of budget deficits to the public’s attention, which encouraged Bill Clinton to reduce the annual deficit while he was president (even to the point of running a budget surplus in 1998–2001). Though it is still unlikely that a third-party candidate has a chance to win (on July 30, RealClearPolitics’s poll average for July 18-29 put Johnson at 7.3% and Stein at 3.0%), perhaps one of these candidates will be seen as playing the role of spoiler, like Ralph Nader when he ran as the presidential candidate for the Green Party in 2000.

Given the possibility that Johnson and Stein may be more prominent than third-party candidates usually are, it’s worth paying attention to how they campaign. This article looks at how Johnson and Stein used Facebook and Twitter over the past several months, comparing their activity with candidates from major parties. (I chose Stein and Johnson because they appear on enough state ballots that they could win the 270 electoral votes needed to be elected. Other third party candidates do not appear on enough state ballots to win the election.) The Illuminating 2016 project uses computational methods to provide a data-driven look at how the candidates use Facebook and Twitter. Our algorithms classify each message by what it is trying to do: for example, calling the reader to engage in some type of action, providing information in a neutral manner, or advocating for or attacking a candidate. We break some of these categories down further, for example classifying attacks as focused on a candidate’s image or their issues.

Our data collection for Johnson begins on January 6th, 2016 and our collection for Stein begins on May 5th, 2016. Generally speaking, Stein uses social media as much as or more than major party candidates; Johnson, on the other hand, is far less active. For example, in the month of June, Stein was the most active presidential candidate on social media, posting 785 messages. Clinton followed with 731 messages. Trump sent 516, while Johnson posted only 221 times. This trend holds looking at May through July: Stein posts more than 3 times as often as Johnson does.

Stein-messagetype clinton-messagetype
johnson-messagetype Trump-messagetype

 

Stein’s strategy

The Green Party is a progressive party with a strong focus on environmental issues. The 2014 Green Party Platform describes the party as “committed to environmentalism, non-violence, social justice and grassroots organization.” The Platform advocates “safe, legal abortion”; “affirms the rights of all individuals to freely choose intimate partners”; calls for “thoughtful, carefully considered gun control”; proposes “shifting tax from individuals to corporations” and “taxing ‘bads’ not ‘goods’”; and suggests reducing the federal debt while funding “our environmental and social needs.”

Even before receiving her party’s nomination, Stein was actively pushing the Green Party message out through Twitter and Facebook. By early July, it became clear that Hillary Clinton would be the Democratic nominee after a hard-fought Democratic primary campaign where Vermont Senator Bernie Sanders tried to convince Democrats to support him instead of Clinton in order to advance a more progressive agenda for the Democratic Party. Since Sanders endorsed Clinton on July 12th, Stein has worked hard to convince Sanders’ supporters that they should shift their support to her as the standard-bearer of true progressive policies and as a genuine outsider. A few hours after Sanders’ endorsement of Clinton, Stein took to Twitter to call on his supporters to join her to “keep the political revolution going,” saying “We don’t need the Democrats.” In her attempt to win over the “Berners,” did Stein adopt a social media strategy like Sanders?

Stein-tweet1

 

Looking at overall numbers from January 1 to July 31, her strategy does not look that similar to Sanders’. In many ways, it appears to be closer to Clinton’s. For example, consider calls to action—messages that ask people to take some action on behalf of the campaign, such as sharing a campaign message with their friends or attending a campaign event. In a recent study, the Illuminating 2016 project found that Sanders used considerably more calls to action than Clinton. Stein’s use of calls to action (16%) is closer to Clinton’s (15%) than to Sanders’ (21%). Stein’s use of informative messages (19%) is also more similar to Clinton’s (18%) than Sanders’ (13%).

clinton-messagetype2 sanders-messagetype2 stein-messagetype2

If you look at types of strategic messaging, the story is similar. Of Clinton’s strategic messages, 61% were advocacy, while 39% were attack; 71% of Sanders’ were advocacy, while 29% were attack; 62% of Stein’s were advocacy, while 38% were attack. While all three of these candidates advocated more often than they attacked, Sanders went on the attack substantially less frequently than Clinton or Stein. Given Stein’s hard push for Sanders’ supporters, it’s somewhat surprising that her social media strategy is in many ways closer to Clinton’s than to Sanders’.

As the Illuminating 2016 project has found, the Democrats in general talk about issues in their strategic messaging more than Republicans do. That said, Clinton still advocates or attacks on the basis of image more often than issues (see here for how we use the terms “image” and “issue”), with 53% of her strategic messages focusing on image rather than issues. Sanders, on the other hand, advocates and attacks on the basis of issues almost twice as often (63%) as he does on the basis of image (37%). Here, Stein charts her own course, splitting the difference: 47% of her strategic messages focus on image, leaving a slight majority to focus on issues.

Stein also does not closely mirror Clinton’s or Sanders’ strategy when it comes to types of calls to action. Clinton encourages far more digital engagement (49%) than Sanders (30%) or Stein (29%). Sanders encourages people to get out and vote considerably more (30%) than Clinton (23%) or Stein (21%). Stein encourages more traditional engagement (36%) than Clinton (20%) or Sanders (29%). Each campaign chose to emphasize different types of engagement, perhaps reflecting larger campaign strategies.

For example, Stein encourages traditional engagement to help her campaign gain access to the ballot in as many states as possible (as of August 7th, Stein is on the ballot in 24 states and in Washington, D.C.).

stein-tweet2

Sanders sent more messages encouraging people to vote, perhaps reflecting a concern that many people who support him were less likely to vote, especially during the primaries.

sanders-tweet

Clinton may be less concerned with encouraging traditional engagement (like volunteering) or voting because she has in a place a strong field organization for mobilizing supporters in key states. Instead, she has used Facebook and Twitter to encourage people to learn more about her by participating in Q&As or sending in their support for her where others could see it, creating a climate of positive opinion around her candidacy.

clinton-tweet

Stein’s activity on Facebook and on Twitter are different in several noteworthy ways. First, she sent far more messages on Twitter (2265 between May 5th and July 31st) than on Facebook (359 during the same period). Second, more of her messages on Twitter were strategic (63%) than on Facebook (44%); she used Facebook more for informative messages (34% versus 17% on Twitter).

stein-messagetype3 stein-messagetype4

So far in the campaign, Stein’s use of the different types of messages has remained fairly consistent. While she has sent more Facebook and Twitter messages each month than the previous month, the relative frequencies of each type of message have not changed much. It’s still early in her campaign, but so far it looks like her campaign has found a social media strategy they like and they’re sticking with it.

stein-over-time

 

Johnson’s strategy

The Libertarian Party is the party of small government, sometimes described as socially liberal and fiscally conservative. The 2016 Libertarian Party Platform says this: the “government should be kept out of the matter” of abortion; “consenting adults should be free to choose their own sexual practices and personal relationships”; the government should not make laws restricting or monitoring ownership of firearms; there should be no income tax; and the government should not be allowed to spend more money than it brings in.

While Stein works hard to attract Sanders’ supporters, positioning herself as an alternative to the Democratic nominee, Johnson’s positioning is less clear (social liberalism and fiscal conservatism is a strange position in American politics). Some polls suggest that he could pull support from both Clinton and Trump in a three-way race.

Johnson generally uses social media less than other candidates. He also uses it in unique ways. Johnson uses calls to action more often (19%) than Clinton (15%) or Trump (10%). He also uses informative messages (31%) far more often than Clinton (18%), but roughly at the same rate as Trump (29%). He uses strategic messages (40%) far less often than either Clinton (57%) or Trump (47%).

clinton-messagetype3 johnson-messagetype3 trump-messagetype3

Drilling down into types of strategic messages, Johnson advocates (64%) more often than he attacks (36%), and he focuses on image (74%) more often than on issues (26%) when doing either. He attacks less often than do Clinton (39%) or Trump (43%), and he talks about image more often than Clinton (53%) or Trump (71%).

Johnson’s style of engagement is where he really stands out. While Clinton has a strong emphasis on digital engagement and Trump focuses on a combination of traditional, digital, and “get out the vote” engagement, Johnson stresses his media appearances (40%, to Clinton’s 2% and Trump’s 8%).

johnson-tweet

This follows from his campaign goal of getting “earned media”—where media outlets cover Johnson’s campaign, giving him publicity that his campaign does not have to pay for. Johnson seems to want to drive traffic to his media appearances by drawing attention to those appearances on Twitter and Facebook. This allows him to get out his message without paying for advertisements, perhaps leading to a self-reinforcing cycle of increased attention. As a third-party candidate with a smaller pool of people contributing money to his campaign, Johnson cannot afford to put as much money into spreading awareness of his name and policies as Clinton or Trump. Free coverage is particularly important for him.

johnson-greentypes clinton-greentypes trump-green-types

There are noteworthy differences in Johnson’s messages on Facebook and his messages on Twitter. Johnson sends far more messages on Twitter (715 between January 1st and July 31st) than on Facebook (276 messages during the same time). On Facebook, more than 50% of Johnson’s messages are informative, with only 24% being strategic. On Twitter, 40% of his messages are strategic and only 31% are informative. Like Stein, Johnson uses Facebook more frequently for informative messages and Twitter more frequently for strategic messages.

johnson-messagetype4 johnson-messagetype5

Unlike Stein, Johnson has emphasized different types of messages at different points in the campaign. In the months before the Libertarian National Convention, Johnson increasingly used strategic messages, highlighting his strengths and others’ weaknesses. Beginning in late May, Johnson began to use social media to inform his followers of media appearances, coinciding with a decrease in the frequency of strategic messages. This use of informative messages matches with his “earned media” strategy mentioned above (many of the informative messages inform readers of his media appearances). Other categories show more moderate changes over time, including a decline in the frequency of calls to action and conversational messages and an increase in ceremonial messages.

johnson-over-time

Conclusions

We might expect third-party candidates to be avid users of social media to get their message to the public. Compared to traditional campaign strategies, social media is cheaper, requires fewer staff members to reach the same size audience, and does not rely on traditional media to amplify the message. This analysis reveals that Johnson isn’t using social media nearly as much as the other candidates, though, missing an opportunity to engage his supporters directly.

This analysis also suggests that Stein isn’t simply Sanders 2.0. Rather than emulating his campaign’s social media strategy, Stein has acted more like Clinton on Facebook and Twitter. She may be calling for Sanders’ supporters to join her, but she isn’t using the social media strategies that helped Sanders start his revolution.

Sam Jackson is a PhD candidate at Syracuse University’s Maxwell School and a research assistant on the Illuminating 2016 project. For more on Illuminating 2016, visit their site.

Curious Communities: An online engagement platform meets face-to-face outreach

“Would you like to be on the radio?” The outreach producer for WBEZ’s Curious City project approaches residents with variations of this question as I shadow him in a park on Chicago’s South Side. He is not here to record a vox pop or to get person-on-the-street reactions to the latest news. Instead he is inviting people to share questions they want answered about any aspect of life in the Chicago region.

wenzel1  

Since 2012, the Curious City project has been inviting Chicago public radio listeners to go online and nominate and vote on questions that they want a reporter to explore. A mix of staff and freelance reporters produce radio features about the questions that are selected, sometimes involving the question-askers in the storytelling process. The project was founded by Jennifer Brandel, who went on to set up Hearken, a digital platform that allows media outlets to adapt their own interactive engagement projects—and is now used by 44 state and regional broadcasters across the U.S.

wenzel2

Using Hearken, Curious City has produced a number of traffic-generating stories for WBEZ—exploring heavy topics like what happened to the people displaced by the construction of a major expressway, as well as lighter fare like the origins of the Chicago accent. Question-askers, though, have tended to come from areas of metro Chicago that are public radio strongholds. Seeking to expand their reach, the team is undertaking a foundation-supported initiative to engage potential audiences from areas of the city where questions have not been coming—primarily African American and Latino neighborhoods, as well as some predominantly white suburbs. The team openly acknowledges that their efforts are experimental. They are trying out a range of offline approaches—direct outreach versus outreach mediated by community institutions. They’re even seeing if it makes a difference whether the producer soliciting questions uses a microphone and recorder or pen and paper. In the end they will compare and see which tactics prove most successful at generating “novel” questions.

For the next several months, I will be following this project, with the support of the Tow Center, to explore whether this initiative has an effect on the local news communication infrastructure, and what the initiative suggests about journalistic norms regarding collaboration with audiences. I will interview journalists, editors, question-askers, community stakeholders, and residents of areas targeted by the outreach campaign.

Initial field outings have taken us to places like Jesse Owens Park in the South Side’s Pill Hill neighborhood, where we met a woman getting a golf lesson. When invited to share a question, she responded with a series of thoughtful queries about the distribution of resources between Chicago’s North and South sides. But afterwards she acknowledged she had been surprised to see us. “Quite honestly, I was like, ‘Why are these white people over here?’” she laughed. While she had never heard of the Curious City project, she liked the idea of journalists physically venturing out to get the perspectives of residents, and genuinely learning about her community. She complained that media representations of Chicago’s South Side tended to paint a monolithic picture of violence, when the reality was a tapestry of very different neighborhoods.

Sentiments like hers echo perspectives documented by a prior Tow Center study on community-based solutions journalism that myself and colleagues from the Metamorphosis research group conducted in South Los Angeles last year. Focus group participants told us they were frustrated with how media coverage stigmatized their neighborhoods. They suggested they largely welcomed reporting that took a more problem-solving approach to exploring community challenges, and that more could be done to engage residents in the process. The study called for foundations and media outlets to do more to support the process of listening to communities.

Curious City’s initiative seems to be combining Hearken’s digital platform with old-school pavement pounding outreach. So far, following their efforts is raising numerous questions about journalistic approaches to participatory media, and relations between public media and marginalized publics. What makes a good question and what happens when the burning thought on a resident’s mind is of greater concern than a question? How do producers and reporters navigate power dynamics and differences of race and class when they are often coming into a community as an outsider? How can the question-asking process be something of value not only for the primary media outlet, but also for community institutions and hyperlocal and ethnic media?

The project also offers an opportunity to examine a media-driven effort to strengthen what communication infrastructure theory (CIT) calls the local “storytelling network.” CIT researchers have previously found that communities are more cohesive when they have stronger links between residents, local media, and community organizations—and all of these actors share an understanding of what is happening in the community. Residents who connect to strong storytelling networks tend to have higher levels of civic engagement and self-efficacy. By reaching out to residents and community groups, WBEZ may be altering the storytelling network. However, there are likely to be barriers of professional culture and language that result in mutual skepticism and incomplete communication.

In a future blog, I will report back how Curious City assesses its online and offline attempts to connect with new communities of Chicago metro residents, what those residents are curious about, and how they would like media to engage with their communities.

Of Twitter, time, and talking: Reflections on interviewing political journalists

Journalists are busy people. It can be hard enough to get a hold of them and even more difficult to get them to meet for coffee as participants in a research project. I braced myself to weather the storm I was certain would come as I embarked upon my project’s field work. After all, I thought, I am about to enter the news industry as one of the many players to compete for a journalist’s attention. To my surprise, things took a slightly different turn.

I recently wrote an introductory blog post about my Tow Fellow project, “Beyond 140 characters.” The piece kicked off a mini-series of blog posts that will outline the project’s key findings on how, why, and under what circumstances political journalists engage with Twitter and which outcomes (both actual and sought after) journalists’ efforts on the platform yield. Here, “engagement” refers to all of journalists’ considerations and activities related to designing, managing, and monitoring a Twitter profile for journalistic and non-journalistic purposes, focusing primarily on active, but also on passive uses of the platform (e.g. tweeting and interactions with other platform users vs. merely following other Twitter profiles or discussions without publishing or sharing any content). This post is a reflection on my field work and provides transparency for how data was gathered. But it also discusses what it was like to interview political journalists, and this tells us something about the myriad intricacies of their occupational realities in a time of post-industrial journalism.

If you’ve ever done research, you know that the choice of method determines the kind of data you obtain, and, ultimately, how this allows you to shed light on your chosen topic. For the “Beyond 140 characters” project it quickly became clear that if I wanted to find out about journalists’ subjective experiences and perceptions of Twitter, I needed to speak with them directly. Expert interviews became the method of choice. This qualitative research technique allows for in-depth inquiries into subjects’ individual perspectives and points of view that can be difficult to gain access to via other methodologies. Because of the semi-structured, conversational style of the interview, interviewees may speak more readily and spontaneously about the meanings and factors that motivate some of their choices and behaviors.

Access, recruiting, and the art of pleasant persistence

The sampling rationale followed four pre-defined criteria. First, as this study focuses on legacy media organizations, each journalist had to work for one of the top 25 commercial broadsheet newspapers or top three cable news channels in the United States. Second, selected journalists had to specialize in the genre of political news, as ascertained by a combination of news organizations’ staff pages and recurring authorship of political news stories. Third, due to the study’s primary concern with active Twitter usage, journalists had to have a minimum amount of platform engagement (i.e. at least 10 tweets per week during a select period). Fourth, journalists were selected in a manner so as to reflect aspects of diversity within their occupational group (e.g. age, gender, professional socialization, and position within the employing organization’s hierarchy).

I reached out to more than 100 journalists and often followed up three more times. Some were unresponsive. Some declined (either because they didn’t want to contribute, were too busy, or because their news organization had explicitly told them not to participate). Some accepted. Some interviews fell through because of continuing scheduling conflicts, others required a few attempts as journalists were pulled into covering stories as they emerged. The news cycle doesn’t stop, as one journalist reminded me when we were trying to set up our conversation:

We can talk, assuming news doesn’t break.

The digital age has fundamentally changed the way journalists do their jobs and the online environment more often than not exacerbates existing pressures. While journalists have more visibility than ever before, this does not necessarily mean they are easily accessible or readily available. Even among journalists who spoke with me, time constraints remained a central reason why some were initially conflicted about contributing to my study. One journalist later outlined those pressures and what it is like to be a journalist today:

Intense. Intense demands to provide content. Intense demands to share information all the time. Intense demands to be correct and accurate. And intense demands to promote your material; to promote it across television, Twitter, radio, TV and print and the web. And that’s a lot. […] Now you have to share it in a compelling way, you have to tell the story on Twitter.  You have to tell it in other ways.  So the demands on your time are significant.

The final sample was comprised of 26 participants, of which 24 worked in editorial staff roles and two in editorial leadership. 23 journalists were employed by a broadsheet newspaper, but only three worked for a cable news channel, somewhat limiting the insights into the possible diversity of perspectives and experiences among broadcast journalists. I interviewed 20 male and six female journalists. The following graph shows the sample distribution by age group and gender:

 

Sample characteristics_age group and gender

 

The interview as a platform for journalistic reflection

For a researcher, participant-based data collection can be intense. You are always on and ready to (even spontaneously) fit into journalists’ schedules. You are trying to make it as easy as possible for them to meet with you (inadvertently becoming a quasi-connoisseur of a news organization’s local coffee shop scene) or talk on the phone (e.g. taking calls when they are in between meetings, while commuting or traveling). While consistency across interview modes and settings is desirable, there are practical challenges. Realistically, you are far from being on top of their list of priorities and you take the chances you get.

Once I got the chance to interview those 26 journalists, a curious thing happened. Many of them suddenly overrode their previous concerns centered on time constraints (along the lines of “your project sounds fascinating, but it’s just so busy right now” or “I would only have 10 to 15 minutes max”). The vast majority of journalists ended up speaking with me for much longer than they had initially said they were able to.

Take a look at the following graph that visualizes interview length based on interview mode for all 26 study participants:

 

Tableau_interview mode and interview length

 

As you can see, no interviews were shorter than 25 minutes. In fact, over half were longer than 45 minutes and almost a third even lasted up to one and a half hours. Unsurprisingly, face-to-face conversations tended to be longer than phone calls. As a matter of principle, this is not to say that longer interviews are always more insightful. But we may reasonably expect that a 90-minute conversation allows greater opportunity to ask questions, clarify statements and follow up, and thus yield richer, more in-depth data than a 15-minute chat.

Overall, the vast majority of journalists confirmed that Twitter has long become pertinent to their everyday-work. Yet, two distinct realities of engagement and discourses about it emerged, which perhaps relate to the substantial difference between journalists’ projected availability and actual time spent speaking with me.

1. Some journalists have a carefully curated presence on the platform. Their engagement stems from deliberate and conscious efforts and is often goal-oriented. These journalists make a substantial and strategic investment into the platform, and Twitter is something they feel they genuinely have a stake in. Naturally, they have a lot to say about it, but they rarely get a chance to discuss the distinct considerations, choices and evaluations that shape their engagement. One journalist explained:

I mean you’re trying to not only create a news source with your Twitter account, you’re trying to cultivate your own brand as a reporter; your reputation.  And Twitter is a useful tool for building your professional reputation because if you think about it, you have immediate access to some of the most influential people in the country and your followers to their phones.  They’re reading you in real-time.  So not only can you offer them smart analysis and reporting, you can also show about where you went to school or you can show about the kind of things you do once in a while with your life.  And it is the face you give to the public.

2. Other journalists are less strategic and preoccupied with their presence on Twitter. One journalist told me:

[I] thought about some things and others I just roll… just roll with.

Many admitted that the interview provided them with a rare opportunity for reflection and offered a platform for contemplating their relationship with and approach to Twitter, away from their workplace and digital lives. For example, at the end of the interview, one journalist said:

It’s hard to believe… we just talked for almost an hour and a half. I guess I had so much more to say than I realized. You know… You know in my job I don’t really get the chance to think about many of these things.

Learning from the content and context of interviews

To conclude, I would like to highlight two key take-aways from the field work stage of this project. First, prepare to be persistent, continue to follow up with potential interviewees, and accept that some journalists cannot or may not want to speak with you. You rely on journalists’ voluntary participation in your project, and many may be extraordinarily generous with their time, providing you with rich data for research. Second, consider the context of your interviews, e.g. the interviewee’s motivations to speak with you, as well as spatial, temporal and social aspects of the interview, etc. This will help you to be reflexive about the interview content, but also aide in better understanding journalists’ contemporary occupational environment and how they find themselves in a web of demands, risks and opportunities. This adds an important perspective to the subsequent stages of data analysis and interpretation.

Finally, I am utterly grateful to those 26 individuals who spent their valuable time telling me about their engagement and experiences with Twitter.

I will soon be blogging about the project’s key findings. So watch this space for the next post in the “Beyond 140 character” series.

[Image credit: E. Vargas, CC BY-SA 2.0]

What will the Internet of Things do to journalism?

Front_of_Raspberry_Pi

According to Cisco, the number of connected objects is expected to reach 50 billion by 2020, equating to 6.58 connected devices per person. They are all controlled by tiny computers that communicate with each other, in an ecosystem commonly known as the “Internet of Things” (IoT).

The IoT has implications for two distinct aspects of journalism – newsgathering and consumption. Smart devices connected to each other can be used to provide better context to a story, such as data on traffic, weather, population density or power consumption.

Innovation Day 

My team at the Associated Press recently provided our engineers with Raspberry Pis – small, easily programmable computers – and access to various sensors, so they could build innovative data-gathering prototypes and display them to coworkers.

We also explored cloud-based platforms, like Amazon’s IoT Cloud, that manage both smart devices and the terabytes of data generated by them, and that facilitate meaningful analysis and decision making.

“How can we make the Internet of Things revolution work for AP, both for our products and our journalism?” asked Vince Tripodi, our vice president of research and development. “That’s what we’re trying to find out.”

A few ideas for how news organizations can incorporate the Raspberry Pis and sensors into their reporting quickly materialized:

– We can monitor vibration and noise from entertainment and political venues to identify the most popular songs at a concert, or the biggest plays of a game, or even the quotes that resonate the most at campaign rallies.

– We can measure water quality in Rio de Janeiro or air quality in Beijing, validating data from environmental protection agencies. More broadly, we can track climate change through conditions of drought or other macro events.

– We can monitor vibrations to measure the impact of construction sites and how they affect nearby residents and businesses, or foot traffic at new and current public transportation stops to gauge their usage.

Beyond reporting

These new technologies will allow journalists to break more stories and dig deeper into them, further closing the gap between the media and technology industries. It’s not just the gathering of news that promises to be affected, though – how audiences consume news will also undoubtedly change.

Key drivers of the growing Internet of Things are connected cars and smart homes. The experience of talking to a dashboard in your car or asking a device at home to tell you the latest news all depends on personalized voice recognition and natural language processing.

As adoption of these smart devices grows, there will still be privacy, security and technical concerns that need to be worked through. The inconsistency of available data in lower population areas also represents a major challenge.

But it should be clear by now that the Internet of Things, like the internet in general, won’t simply go away. As media companies, we need to start thinking about how these new technologies can help us better inform the world.

Francesco Marconi is the Strategy Manager for The Associated Press and an innovation fellow at the Tow Center. Follow him @fpmarconi

Using machine learning to classify presidential candidate social media messages

Since presidential campaigns have incorporated social media into their strategic messaging, it has become more challenging for journalists to cover the election in depth, because of the large amount of data generated by candidates and the public every day. Journalists tend to focus on single quotes or tweets rather than providing analysis and reporting on the aggregate of messages on social media. But single tweets may not give people a full appreciation for the style of campaigning or the substance of the rest of the tweets.

In order to get a sense of what the candidates and public are actually saying and how candidates communicate over time, we have taken a computational approach to predict categories of candidate-produced tweets and posts (as described in a blog post introducing the Illuminating 2016 project). We have been working on a system that automatically classifies each message into a category based on what the message is trying to do: urge people to act, change their opinions through persuasion, inform them about some activity or event, honoring or mourning people or holidays, or on Twitter having a conversation with members of the public. This blog aims to introduce you to how we use machine learning to predict category for candidates’ generated messages. The data currently presented on Illuminating 2016 is accurately categorized 77% of the time. For some categories, the accuracy is up to 84%, such as call-to-action, and strategic message types of advocacy and attack.

To predict presidential campaign message type, we used gubernatorial campaign data from 2014 to build initial categories and to train machine-learning models. And then, we tested the reliability of the best models built from the gubernatorial data and applied them to classify messages from the 2016 presidential campaign in real time. We’ve been collecting all of the announced major party candidates’ Twitter and Facebook posts since they declared their presidential bids. In all we have filled 6 servers with 24 presidential candidates’ social media messages, and of course we’re still collecting. The diagram below demonstrates how we use machine learning to train the models.

Diagram 1 Models training
Diagram 1: Models training

To understand candidates’ social media message strategy, we collected the Facebook and Twitter messages produced by the campaign accounts of 79 viable candidates who ran for governor. The collections started September 15th when all states had completed their primaries and shifted into the general election phase, and continued through November 7th, three days after the election. We ended up with a total of 34,275 tweets and 9,128 Facebook posts. We categorized these messages by their performative speech categories of strategic message, informative, call-to-action, and ceremonial. Besides these, we also added non-English, and conversational categories (conversational only applies to Twitter). These categories allow us to understand when candidates advocate for themselves, go on the attack, urge supporters to act, and use the affordances of social media to interact with the public.

These categories were developed deductively and were revised based on inductive analysis. We trained annotators and refined the codebook over several rounds until two or more annotators could look at the same message and agree on the category. We generate an inter-coder agreement score to determine how easy or hard it is for humans to categorize the messages, and also to make sure our categories are clearly defined and mutually exclusive as much as possible. Our score of that agreement is Krippendorff’s Alpha of .70 or greater on all categories. After annotating data independently, annotators developed gold standard annotations, which means that two coders categorized the same messages and then where they disagreed on a category, they talked it through and decided which was the “best” category for that message. This labeled Twitter and Facebook messages by the candidates, generating 4,147 tweets and 2,493 Facebook messages as gold standard data.

We used these gold standard data as training data to build models, and then applied the best models to un-labeled candidates’ messages in Facebook and Twitter. Prior to models building, we represented text,added relevant language features and political characteristics for models training purpose. For example,

  • We replaced instances of user tagging (e.g. @abc) and URLs (e.g. http://abc) with the general features USERNAME and URL;
  • We removed stop words;
  • We used unigram and bigram document representation;
  • We transformed messages to a sequences of boolean features;
  • We added feature starting with @mention for twitter data;
  • We used part of speech tagging, e.g. tweets or Facebook posts starting with verb;
  • Given characteristics of election data, we also added some relevant political features, e.g. political party (Republican, Democrat, Third Party) to help the model training.

For algorithm building, using Scikit-Learn, we performed several experiments with the following multi-class classification algorithms: Support Vector Machine (SVM), Naïve Bayes (NB), MaxEnt/Logistic Regression, and Stochastic Gradient Descent (SGD). All classification tasks were evaluated with 10-fold cross validation. We use a micro-averaged F1 score to measure prediction accuracy (F1 score reaches its best value at 1 and worst at 0). For Twitter data, the best micro-averaged F1 score is 0.72, as shown in Table 1, by using a SGD classifier with a Boolean feature, with tweets feature starting with @_username, verb first and party feature. The F1 value of strategic message is up to 0.75. For Facebook data, the best micro-averaged F1 value is 0.73, by using Linear SVC classifier with a Boolean feature and party feature. The F1 value of call-to-action is up to 0.80. By comparison, the majority baseline for Twitter data is 37.6% (1559/4147) and 40.1% (999/2493) for Facebook. It should be noted that the F1 score of ceremonial messages is low. The reason for the lower score for this category is that there are far fewer of these messages, and they often express a wider range of features, making them harder to classify.

Table 1 Machine prediction performance for Main Categories in Tweeter and Facebook
Table 1: Machine prediction performance for Main Categories in Tweeter and Facebook

For Strategic Message type prediction, we trained the classifiers with training data labeled as Strategic Message: 1,559 tweets and 860 Facebook posts. Each message is classified as either Advocacy or Attack. As shown in Table 2, the micro-averaged F1 scores of Twitter and Facebook data are 0.80 and 0.84. By comparison, the majority baseline for Twitter data is 69.4% (1082/1559) and 62.8% (540/860) for Facebook. Similarly, our Strategic Message’s focus classifiers were trained with the messages labelled as Strategic Message as well. Each message can be either Image, Issue or Endorsement. As shown in Table 3, the micro-averaged F1 scores of Strategic Message focus category in both Twitter and Facebook are 0.77. By comparison, the majority baseline for Twitter data is 48.2% (751/1559) and 50.6% (435/860) for Facebook.

Table 2 Machine prediction performance for Types of Strategic Messages in Tweeter and Facebook
Table 2: Machine prediction performance for Types of Strategic Messages in Tweeter and Facebook

Table 3 Machine prediction performance for Focus of Strategic Messages in Tweeter and Facebook
Table 3: Machine prediction performance for Focus of Strategic Messages in Tweeter and Facebook

We can see that all the micro-averaged F1 scores reported above are much higher than the baseline scores. This suggests that the machine-annotating models have been trained to predict candidate-produced messages well.

We are still testing the reliability of the current best models on presidential campaign data. When using the above reported best models on 2989 human-corrected presidential Twitter data and 2638 Facebook data, we found that generally the models still worked well, as shown in Table 4. But, F1 score of Conversational category dropped 20%. We guess that there should be some differences between gubernatorial campaign data and presidential campaign data in this category, and we are currently investigating the possible reasons.

Table 4 Machine prediction performance of presidential data by using gubernatorial data as training data
Table 4:
Machine prediction performance of presidential data by using gubernatorial data as training data

We also did experiments only including presidential data as training data to test the model performance. For Facebook, we found that the model performs pretty well on predicting strategic messages (F1=0.77) and call-to-action (F1=0.86), as shown in Table 5.

Table 5 Machine prediction performance of presidential data by using presidential data as training data
Table 5:
Machine prediction performance of presidential data by using presidential data as training data

Our next step is to do more experiments to improve the model, e.g., experimenting with binary classification, adding opinion classification and sentiment classification. We are applying the best models to predict category for messages generated by candidates in the 2016 presidential campaign now. In our Illuminating 2016 website, reporters and the public can understand presidential campaign messages type instantaneously. We are pulling public commentary on the election from social media and categorizing them now. You will see public commentary analysis in our website in August.

Thanks to Sikana Tanupambrungsun and Yatish Hegde at the School of Information Studies at Syracuse University for data collection and model training. 

Digital 8bit House
http://materinstwo.com/uzi-pri-beremennosti/320-transvaginalnaya-ehografiya-v-pervom-trimestre-beremennosti.html