Tag Archives: online journalism book

What you need to know about the laws on harassment, data protection and hate speech {UPDATED: Stalking added}

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The following is taken from the law chapter of The Online Journalism Handbook. The book blog and Facebook page contain updates and additions – those specifically on law can be found here.

Harassment

The Protection From Harrassment Act 1997 is occasionally used to prevent journalists on reporting on particular individuals. Specifically, any conduct which amounts to harassment of someone can be considered to a criminal act, for which the victim can seek an injunction (followed by arrest if broken) or damages.

One example of a blogger’s experience is illustrative of the way the act can be used with regard to online journalism, even if no case reaches court. Continue reading →

My online journalism book is now out

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The Online Journalism Handbook, written with Liisa Rohumaa, has now been published. You can get it here.

I’ve been blogging throughout the process of writing the book – particularly the chapters on data journalism, blogging and UGC – and you can still find those blog posts under the tag ‘Online Journalism Book‘.

Other chapters cover interactivity, audio slideshows and podcasting, video, law, some of the history that helps in understanding online journalism, and writing for the web (including SEO and SMO).

Meanwhile, I’ve created a blog, Facebook page and Twitter account (@OJhandbook) to provide updates, corrections and additions to the book.

If you spot anything in the book that needs updating or correcting, let me know. Likewise, let me know what you think of the book and anything you’d like to see added in future.

Why did you get into data journalism?

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In researching my book chapter (UPDATE: now published) I asked a group of journalists who worked with data what led them to do so. Here are their answers:

Jonathon Richards, The Times:

The flood of information online presents an amazing opportunity for journalists, but also a challenge: how on earth does one keep up with; make sense of it? You could go about it in the traditional way, fossicking in individual sites, but much of the journalistic value in this outpouring, it seems, comes in aggregation: in processing large amounts of data, distilling them, and exploring them for patterns. To do that – unless you’re superhuman, or have a small army of volunteers – you need the help of a computer.

I ‘got into’ data journalism because I find this mix exciting. It appeals to the traditional journalistic instinct, but also calls for a new skill which, once harnessed, dramatically expands the realm of ‘stories I could possibly investigate…’ Continue reading →

Podcasting: the experiences of Bagel Tech News

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Bagel Tech News podcast

As part of the research into a forthcoming book on online journalism (UPDATE: now published), I interviewed Ewen Rankin of independent podcast Bagel Tech News. Here are his responses in full:

The background

My background is as a commercial photographer. I started life in graphic design and quickly moved to shooting photographs for the agency at which I worked. It was kind of a lucky transition as I wasn’t much cop as a graphic artist. I took fairly low level stuff to start with (picture business cards were all the rage in the 80s) and then moved to more commercial work shooting the advertising shots for Pretty Polly and Golden Lady tights in about 1988.

I start broadcasting in July 2008 and after two weeks Amber Macarthur made us Podcast of the Week on the Net@Night show with Leo Laporte. Listenership rose and we began to grow.

The Daily News show was published… daily until November 2008 and then I started publishing the BOG Show with Marc Silk, and was opened by Andy Ihnatko on 30th November 2008. I removed Marc from the show in Christmas 2009 and installed a ‘Skype Wall’ in January 2010 to run a more panel based show. More shows have been added in the intervening period and the network now has 7 active shows Continue reading →

5 data visualisation tips from David McCandless

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Here’s another snippet from my data journalism book chapter (now published). As part of my research David McCandless, author of the very lovely book and website Information is Beautiful gave  his 5 tips for visualising data:

  1. Double source data wherever possible – even the UN and WorldBank can make mistakes
  2. Take information out – there’s a long tradition among statistical journalists of showing everything. All data points. The whole range. Every column and row. But stories are about clear threads with extraneous information fuzzed out. And journalism is about telling stories. You can only truly do that when you mask out the irrelevant or the minor data. The same applies to design which is about reducing something to its functional essence.
  3. Avoid standard abstract units – tons of carbon, billions of dollars – these kinds of units are over-used and impossible to imagine or relate to. Try to rework or process units down to ‘everyday’ measures. Try to give meaningful context for huge figures whenever possible.
  4. Self-sufficency – all graphs, charts and infographics should be self-sufficient. That is, you shouldn’t require any other information to understand them. They’re like interfaces. So each should have a clear title, legend, source, labels etc. And credit yourself. I’ve seen too many great visuals with no credit or name at the bottom.
  5. Show your workings – transparency seems like a new front for journalists. Google Docs makes it incredibly easy to share your data and thought processes with readers. Who can then participate.

Data journalism pt5: Mashing data (comments wanted)

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This is a draft from a book chapter on data journalism (part 1 looks at finding data; part 2 at interrogating data; part 3 at visualisation, and 4 at visualisation tools). I’d really appreciate any additions or comments you can make – particularly around tips and tools.

UPDATE: It has now been published in The Online Journalism Handbook.

Mashing data

Wikipedia defines a mashup particularly succinctly, as “a web page or application that uses or combines data or functionality from two or many more external sources to create a new service.” Those sources may be online spreadsheets or tables; maps; RSS feeds (which could be anything from Twitter tweets, blog posts or news articles to images, video, audio or search results); or anything else which is structured enough to ‘match’ against another source.

This ‘match’ is typically what makes a mashup. It might be matching a city mentioned in a news article against the same city in a map; or it may be matching the name of an author with that same name in the tags of a photo; or matching the search results for ‘earthquake’ from a number of different sources. The results can be useful to you as a journalist, to the user, or both.

Why make a mashup?

Mashups can be particularly useful in providing live coverage of a particular event or ongoing issue – mashing images from a protest march, for example, against a map. Creating a mashup online is not too dissimilar from how, in broadcast journalism, you might set up cameras at key points around a physical location in anticipation of an event from which you will later ‘pull’ live feeds: in a mashup you are effectively doing exactly the same thing – only in a virtual space rather than a physical one. So, instead of setting up a feed at the corner of an important junction, you might decide to pull a feed from Flickr of any images that are tagged with the words ‘protest’ and ‘anti-fascist’. Continue reading →

Data journalism pt4: visualising data – tools and publishing (comments wanted)

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This is a draft from a book chapter on data journalism (here are parts 1; two; and three, which looks the charts side of visualisation). I’d really appreciate any additions or comments you can make – particularly around tips and tools.

UPDATE: It has now been published in The Online Journalism Handbook.

Visualisation tools

So if you want to visualise some data or text, how do you do it? Thankfully there are now dozens of free and cheap pieces of software that you can use to quickly turn your tables into charts, graphs and clouds.

The best-known tool for creating word clouds is Wordle (wordle.net). Simply paste a block of text into the site, or the address of an RSS feed, and the site will generate a word cloud whose fonts and colours you can change to your preferences. Similar tools include Tagxedo (tagxedo.com) and Wordlings (http://wordlin.gs), both of which allow you to put your word cloud into a particular shape.

ManyEyes (manyeyes.alphaworks.ibm.com/manyeyes/) also allows you to create word clouds and tag clouds – as well as word trees and phrase nets that allow you to see common phrases. But it is perhaps most useful in allowing you to easily create scattergrams, bar charts, bubble charts and other forms. The site also contains a raft of existing data that you can play with to get a feel for the site. Similar tools that allow access to other data include Factual (factual.com), Swivel (swivel.com)[see comments], Socrata (socrata.com) and Verifiable.com (verifiable.com). And Google Fusion Tables (tables.googlelabs.com) is particularly useful if you want to collaborate on tables of data, as well as offering visualisation options.

More general visualisation tools include widgenie (widgenie.com), iCharts (icharts.net), ChartTool (onlinecharttool.com) and ChartGo (www.chartgo.com). FusionCharts is a piece of visualisation software with a Google Gadget service that publishers may find useful. You can find instructions on how to use it at www.fusioncharts.com/GG/Docs

If you want more control over your visualisation – or want it to update dynamically when the source information is updated, Google Chart Tools (code.google.com/apis/charttools) is worth exploring. This requires some technical knowledge, but there is a lot of guidance and help on the site to get you started quickly.

Tableau Public is a piece of free software you can download (tableausoftware.com/public) with some powerful visualisation options. You will also find visualisation options on spreadsheet applications such as Excel or the free Google Docs spreadsheet service. These are worth exploring as a way to quickly generate charts from your data on the fly.

Publishing your visualisation

There will come a point when you’ve visualised your data and need to publish it somehow. The simplest way to do this is to take an image (screengrab) of the chart or graph. This can be done with a web-based screencapture tool like Kwout (kwout.com), a free desktop application like Skitch (skitch.com) or Jing (jingproject.com), or by simply using the ‘Print Screen’ button on a PC keyboard (cmd+shift+3 on a Mac) and pasting the screengrab into a graphics package such as Photoshop.

The advantage of using a screengrab is that the image can be easily distributed on social networks, image sharing websites (such as Flickr), and blogs – driving traffic to the page on your site where it is explained.

If you are more technically minded, you can instead choose to embed your chart or graph. Many visualisation tools will give you a piece of code which you can copy and paste into the HTML of an article or blog post in the place you wish to display it (this will not work on most third party blog hosting services, such as WordPress.com). One particular advantage of this approach is that the visualisation can update itself if the source data is updated.

Alternatively, an understanding of Javascript can allow you to build ‘progressively enhanced’ charts which allow users to access the original data or see what happens when it is changed.

Showing your raw data

It is generally a good idea to give users access to your raw data alongside its visualisation. This not only allows them to check it against your visualisation but add insights you may not otherwise gain. It is relatively straightforward to publish a spreadsheet online using Google Docs (see the sidebar on publishing a spreadsheet)

SIDEBAR: How to: publish a spreadsheet online

Google Docs (docs.google.com) is a free website which allows you to create and share documents. You can share them via email, by publishing them as a webpage, or by embedding your document in another webpage, such as a blog post. This is how you share a spreadsheet:

  1. Open your spreadsheet in Google Docs. You can upload a spreadsheet into Google Docs if you’ve created it elsewhere – there is a size limit, however, so if you are told the file is too big try removing unnecessary sheets or columns.
  2. Look for the ‘Share’ button (currently in the top right corner) and click on it.
  3. A drop-down menu should appear. Click on ‘Publish as a web page’
  4. A new window should appear asking which sheets you want to publish. Select the sheet you want to publish and click ‘Start publishing’ (you should also make sure ‘Automatically republish when changes are made’ is ticked if you want the public version of the spreadsheet to update with any data you add.)
  5. Now the bottom half of that window – ‘Get a link to the published data’ – should become active. In the bottom box should be a web address where you can now see the public version of your spreadsheet. If you want to share that, copy the address and test that it works in a web browser. You can now link to it from any webpage.
  6. Alternatively, you can embed your spreadsheet – or part of it – in another webpage. To do this click on the first drop-down menu in this area – it will currently say ‘Web page’ – and change it to ‘HTML to embed in a page’. Now the bottom box on this window should show some HTML that begins with
  7. If you want to embed just part of a spreadsheet, in the box that currently says ‘All cells’ type the range of cells you wish to show. For example, typing A1:G10 will select all the cells in your spreadsheet from A1 (the first row of column A) to G10 (the 10th row of column G). Once again, the HTML below will change so that it only displays that section of your spreadsheet.

Once again, I’d welcome any comments on things I may have missed or tips you can add. Part 5, on mashups, is now available here.

Data journalism pt3: visualising data – charts and graphs (comments wanted)

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This is a draft from a book chapter on data journalism (the first, on gathering data, is here; the section on interrogating data is here). I’d really appreciate any additions or comments you can make – particularly around considerations in visualisation. A further section on visualisation tools, can be found here.

UPDATE: It has now been published in The Online Journalism Handbook.

“At their best, graphics are instruments for reasoning about quantitative information. Often the most effective way to describe, explore, and summarize a set of numbers – even a very large set – is to look at pictures of those numbers.” (Edward Tufte, The Visual Display of Quantitative Information, 2001)

Visualisation is the process of giving a graphic form to information which is often otherwise dry or impenetrable. Classic examples of visualisation include turning a table into a bar chart, or a series of percentage values into a pie chart – but the increasing power of both computer analysis and graphic design software have seen the craft of visualisation develop with increasing sophistication. In larger organisations the data journalist may work with a graphic artist to produce an infographic that visualises their story – but in smaller teams, in the initial stages of a story, or when speed is of the essence they are likely to need to use visualisation tools to give form to their data.

Broadly speaking there are two typical reasons for visualising data: to find a story; or to tell one. Quite often, it is both. Continue reading →

Data journalism pt2: Interrogating data

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This is a draft from a book chapter on data journalism (the first, on gathering data, is here). I’d really appreciate any additions or comments you can make – particularly around ways of spotting stories in data, and mistakes to avoid.

UPDATE: It has now been published in The Online Journalism Handbook.

“One of the most important (and least technical) skills in understanding data is asking good questions. An appropriate question shares an interest you have in the data, tries to convey it to others, and is curiosity-oriented rather than math-oriented. Visualizing data is just like any other type of communication: success is defined by your audience’s ability to pick up on, and be excited about, your insight.” (Fry, 2008, p4)

Once you have the data you need to see if there is a story buried within it. The great advantage of computer processing is that it makes it easier to sort, filter, compare and search information in different ways to get to the heart of what – if anything – it reveals. Continue reading →

Data journalism pt1: Finding data (draft – comments invited)

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The following is a draft from a book about online journalism that I’ve been working on. I’d really appreciate any additions or comments you can make – particularly around sources of data and legal considerations

The first stage in data journalism is sourcing the data itself. Often you will be seeking out data based on a particular question or hypothesis (for a good guide to forming a journalistic hypothesis see Mark Hunter’s free ebook Story-Based Inquiry (2010)). On other occasions, it may be that the release or discovery of data itself kicks off your investigation.

There are a range of sources available to the data journalist, both online and offline, public and hidden. Typical sources include:

Continue reading →