Since the development of computers, CAR has been used by journalists to uncover patterns and trends by examining data. Now, CAR has become a subset of the wider area of expertise known as data journalism – which includes coding, automation, and data visualisation for interactive storytelling.

In our article ‘What is data journalism?‘ we refer to CAR in the context of its role in data journalism. But what is CAR? And how does it differ from data journalism.

• Computer-Assisted Reporting (CAR):
  • Emerged in the late 20th century as journalists began using computers for reporting.
  • Focuses on using databases, spreadsheets, and basic statistical tools to analyse public records, election results, crime reports, etc.
  • Example: A journalist using Excel to analyse government spending records for a piece of investigative journalism.
• Data Journalism:
  • A broader, more modern evolution of CAR that includes data collection, analysis, and data visualisation.
  • Incorporates coding, automation, and interactive storytelling techniques.
  • Often involves using programming languages (Python, R), web scraping, machine learning, and data visualisation tools (Tableau, D3.js).
    • See our ‘Data journalism glossary‘
  • Example: The New York Times’ interactive COVID-19 tracking dashboards or The Guardian’s data-driven investigative reports.
    

Differences between CAR and data journalism:

In short, CAR is an early form of data journalism. While CAR was about using computers for analysis, data journalism has expanded to include sophisticated digital tools, coding, and visual storytelling techniques.

Related articles

What is data journalism?

Good journalism has always been about data

Data journalism – resources and tools

Data journalism glossary

The post Computer-assisted reporting (CAR) first appeared on Media Helping Media.

Fact-checking

In our article ‘Fact-checking and adding context‘ we looked at some of the traditional method for verifying information.

In the piece ‘Beyond fact-checking‘ we went beyond simple verification to applying critical thinking and contextual analysis to our fact-checking.

And in ‘Lateral reading for journalists‘ we looked at methods that have been used by journalist for many years but which have become easier and faster to apply thanks to the internet.

All the methods mentioned above are designed to help journalists weed out misinformation and disinformation in a bid to provide robust, accurate, and factual information.

Now we look at the SIFT method of fact-checking, research, and adding context, which has been developed by Mike Caulfield and is increasingly being used in journalism training and education.

SIFT teaches students how to critically evaluate information online through four steps: Stop, Investigate the source, Find better coverage, and Trace claims, quotes, and media back to the original context.

While it’s primarily aimed at combating misinformation in the digital age, it also offers computer-assisted benefits that can enhance traditional journalism practices. SIFT has been adopted in various educational settings, including media literacy courses and journalism programmes.

The SIFT method provides a straightforward process for evaluating online information. Its four distinct elements are:

• Stop:
  • This initial step emphasises pausing before engaging with any information. It encourages users to resist the urge to immediately share or believe something, and instead, to take a moment to reflect.
• Investigate the source:
  • This involves determining the credibility and background of the source of the information. It encourages journalists to apply “lateral reading,” which means looking at what other sources say about the original source.
• Find better coverage:
  • This step advises seeking out more reliable and trustworthy sources that provide better coverage of the claim or topic. It encourages journalists to look for consensus and expert analysis.
• Trace claims, quotes, and media to the original context:
  • This involves tracking down the original source of a claim, quote, or piece of media to understand its context.

Applying the four steps of the SIFT method leads to a more informed and accurate understanding of online information. Specifically, it aims to produce these key results:

• Increased accuracy:
  • By investigating sources and tracing claims, a journalist is less likely to be misled by false or misleading information.
• Improved source evaluation:
  • The journalist develops the ability to quickly and effectively assess the credibility and reliability of online sources.
• Enhanced contextual understanding:
  • Tracing claims to their original context enables the journalist to make sure that information is not being taken out of context and misrepresented.
• Reduced susceptibility to misinformation:
  • By being aware of emotional responses and actively seeking better coverage, the journalist become less vulnerable to manipulative content.
• Development of critical thinking skills:
  • SIFT fosters a habit of healthy skepticism and critical analysis, which are essential for journalists navigating the complex digital landscape.
• More responsible information sharing:
  • By verifying information before sharing it, the spread of misinformation is reduced.

In essence, the SIFT method is yet another form of fact-checking with an emphasis on speed. It provides a rapid fact-checking framework tailored for the digital age, prioritising quick evaluation of sources, content, emotional triggers, and cross-referencing against reliable information. Its value is amplified by the sheer volume and velocity of information being shared via user-generated content, social media, and algorithmic recommendations.

Related articles

Fact-checking and adding context

Beyond basic fact-checking

Lateral reading

Disinformation and misinformation

The post SIFT for fact-checking first appeared on Media Helping Media.

But what is lateral reading? How does it differ from normal reading? How should journalists apply it to their work? And what are the benefits?

Lateral reading existed long before computers and the internet. In the days of notebooks, pens, typewriters, and paper documents, journalists would have access to a stack of reference books sitting on dusty shelves in the newsroom, which they would consult when a story broke.

These would include well-thumbed encyclopaedias, copies of Who’s Who, and various dictionaries, English language style books, and journalism guide books.

There would also be the newspaper’s own archive of previous editions, see our article on keeping records.

If a journalist didn’t have the book they needed they would put their coat on and nip down to the local library to research information. They would also call any of the newspaper’s contacts who were knowledgeable about the issue being covered.

Of course not all stories required lateral reading. A news editor would often throw a journalist a news (press) release about a forthcoming event or other non-contentious news item and order them to “knock out 250 words on that”.

The journalists might put a call in to any contact mentioned in the news release, but often they would do as they were told and rework the content to keep the news editor happy. After all, they were merely looking for material to fill vacant space on a page. That is not lateral reading.

However, if a news editor wanted a topic to be investigated then the journalist would have to do their research. The order from the news editor would probably be along the lines of “have a dig around this and see what you come up with”. At that point lateral reading kicked in.

Now, in the age of computers and the internet, lateral reading is understood to mean navigating a wide variety of online information simultaneously in order to check-facts and learn more about a topic.

It involves opening multiple tabs in your web browser to investigate the credibility of a source, rather than just reading the information on a single page (which is called “vertical reading”).

When you read ‘vertically’, you stay on the same webpage and assess its credibility based on what you can see. This can be misleading, as biased or unreliable sources often present themselves as trustworthy.

How to apply lateral reading

There are many organisations that have developed courses where journalists can learn about how to apply lateral reading such as Civic Online Reasoning (COR), the News Literacy Project and the Association of College & Research Libraries (ACRL).

But you don’t need to attend a course in order to start applying lateral reading effectively when working online. Try these steps:

1. Open multiple tabs
   • When coming across a claim, an article, or a source, open multiple tabs to:
     • Investigate the website or organisation publishing the information.
     • Research the author or source of the claim.
     • Find other reliable sources covering the same topic.
2. Investigate the source
   • Before trusting information, research the source’s credibility:
     • Search for the organisation on Wikipedia or in news articles.
     • Check their “About” page for affiliations and biases.
     • Look for past credibility issues (fact-checking sites might flag it).
3. Cross-check information
   • Find other reliable sources reporting on the same issue:
     • Use fact-checking websites such as Snopes, PolitiFact, or Reuters Fact Check.
     • Look for government reports, academic sources, or expert commentary.
     • Be cautious if only partisan or obscure sites are reporting a claim.
4. Manipulative framing
   • Compare how different outlets report the same facts:
     • Look at how different sources describe the same event.
     • Consider whether images, headlines, or quotes are used selectively.
     • Be aware of emotionally charged language designed to sway opinion.
5. Social media and user-generated content
   • To fact-check viral claims:
     • Reverse search images to check their origin, using tools such as Google Images and TinEye.
     • Look for verification badges on social media accounts.
     • Check timestamps – an old photo might be falsely used for a current event.

Conclusion

Lateral reading is a skill journalists need to develop in order to help them in their research, fact-checking, and context-building. This in turn helps them maintain their commitment to accuracy and credibility. In an era where misinformation and disinformation can easily mislead audiences, lateral reading remains a powerful tool in the pursuit of truth and responsible reporting.

Related articles

Fact-checking and adding context

Beyond basic fact-checking

News sources and the ‘so what’ factor

The post Lateral reading first appeared on Media Helping Media.

It’s essential for journalists to maintain precision in their use of language, especially when dealing with words that sound or look similar but which carry different meanings.

Journalists not only have to be accurate with their facts, they also need to be clear in their writing. Using the wrong words can confuse the audience and lead to the spread of misinformation.

In his article, The Power of Words, John Allen listed 26 combinations of words that sound or look the similar but have a different meaning, spelling, or both. These words are called ‘homophones’. We have taken John’s list and expanded it to 110 words by referencing several sources which we have included below.

We then fed the list into Google Gemini AI with the prompt to order the words alphabetically as well as adding a sentence to each grouping explaining the meaning of the words. The team at MHM then went through the list, checked the text and revised where necessary.

We hope you find the list helpful. It’s likely we will add to it as new combinations come to light.

• Abuse/Misuse: Abuse is to treat badly; misuse is to use incorrectly.
• Affect/Effect: Affect is to influence; effect is a result.
• Aggravate/Annoy: Aggravate is to worsen; annoy is to irritate.
• Amend/Emend: Amend is to modify; emend is to correct text.
• Amiable/Amicable: Amiable is friendly; amicable is characterised by friendliness.
• Anticipate/Expect: Anticipate is to foresee and act; expect is to believe something will happen.
• Appraise/Apprise: Appraise is to assess value; apprise is to inform.
• Assent/Agree: Assent is to concur formally; agree is to have the same opinion.
• Assumption/Presumption: Assumption is something taken for granted; presumption is an arrogant supposition.
• Assure/Ensure/Insure: Assure is to remove doubt; ensure is to make certain; insure is to protect against risk.
• Aural/Oral: Aural relates to hearing; oral relates to speaking.
• Beside/Besides: Beside means next to; besides means in addition to.
• Biannual/Biennial: Biannual occurs twice a year; biennial occurs every two years.
• Born/Borne: Born is brought into life; borne is carried.
• Breach/Breech: Breach is a violation; breech is the rear part of a firearm or lower rear part of the body.
• Broach/Brooch: Broach is to introduce a topic; brooch is an ornamental pin.
• Can/May: Can indicates ability; may indicates permission.
• Captivate/Capture: Captivate is to attract and hold attention; capture is to seize.
• Censor/Sensor: Censor is to suppress content; sensor is a device that detects.
• Childish/Childlike: Childish is immature; childlike is innocent.
• Chord/Cord: Chord is a musical combination; cord is a thin rope.
• Cite/Sight/Site: Cite is to quote; sight is the ability to see; site is a location.
• Collude/Conspire: Collude is to cooperate secretly; conspire is to plan secretly.
• Common/Mutual: Common is shared; mutual is reciprocal.
• Complement/Compliment: Complement completes or enhances; compliment expresses praise.
• Compose/Comprise: Compose is to create; comprise is to consist of.
• Compulsive/Compulsory: Compulsive is driven by an irresistible urge; compulsory is required by law.
• Comprise/Consist: Comprise is to include or contain; consist is to be made up of.
• Continual/Continuous: Continual occurs repeatedly; continuous occurs without interruption.
• Credible/Credulous: Credible is believable; credulous is gullible.
• Defective/Deficient: Defective has a flaw; deficient lacks something.
• Definite/Definitive: Definite is certain; definitive is conclusive.
• Dependent/Dependant: Dependent is an adjective meaning relying on; dependant is a noun meaning a person who relies on another.
• Desert/Dessert: Desert is a dry area; dessert is a sweet course.
• Diagnosis/Prognosis: Diagnosis is identification of an illness; prognosis is a prediction of its course.
• Discreet/Discrete: Discreet means tactful; discrete means separate.
• Disinterested/Uninterested: Disinterested means impartial; uninterested means not interested.
• Distinctive/Distinguished: Distinctive is characteristic; distinguished is renowned.
• Draft/Draught: Draft is a preliminary version; draught is a current of air or a drink.
• Dual/Duel: Dual means having two parts; duel is a contest between two people.
• Economic/Economical: Economic relates to the economy; economical is thrifty.
• Emotional/Emotive: Emotional relates to emotions; emotive arouses emotions.
• Emigrate/Immigrate: Emigrate is to leave a country; immigrate is to enter a country.
• Empathy/Sympathy: Empathy is understanding feelings; sympathy is feeling pity.
• Emulate/Imitate: Emulate is to strive to equal; imitate is to copy.
• Epidemic/Pandemic: Epidemic is a widespread disease; pandemic is a global epidemic.
• Equable/Equitable: Equable is steady or even; equitable is fair.
• Exhausted/Exhaustive: Exhausted is tired; exhaustive is thorough.
• Fair/Fare: Fair means just or light-coloured; fare is a cost or food.
• Farther/Further: Farther refers to physical distance; further refers to metaphorical distance.
• Fewer/Less: Fewer refers to countable items; less refers to uncountable items.
• Flounder/Founder: Flounder is to struggle; founder is to fail or sink.
• Flout/Flaunt: Flout is to disregard; flaunt is to show off.
• Forbear/Forebear: Forbear is to refrain; forebear is an ancestor.
• Forego/Forgo: Forego is to precede; forgo is to abstain from.
• Fortuitous/Fortunate: Fortuitous is accidental; fortunate is lucky.
• Grand/Grandiose: Grand is impressive; grandiose is exaggeratedly impressive.
• Hang/Hung: Hang is to suspend; hung is the past participle of hang.
• Heroin/Heroine: Heroin is an opiate; heroine is a female hero.
• Hoard/Horde: Hoard is to accumulate; horde is a large group.
• Imaginary/Imaginative: Imaginary is unreal; imaginative is creative.
• Imply/Infer: Imply is to suggest; infer is to deduce.
• Innovation/Invention: Innovation is a new method; invention is a new device.
• Innuendo/Insinuation: Innuendo is an indirect suggestion; insinuation is a subtle hint.
• Inflammable/Inflammatory: Inflammable is capable of catching fire; inflammatory causes inflammation or arouses anger.
• Its/It’s: Its is a possessive pronoun; it’s is a contraction of “it is.”
• Junction/Juncture: Junction is a point of intersection; juncture is a point in time.
• Knell/Knoll: Knell is a sound of a bell; knoll is a small hill.
• Lama/Llama: Lama is a Buddhist teacher; llama is a South American animal.
• Latitude/Longitude: Latitude is distance north or south; longitude is distance east or west.
• Lay/Lie: Lay is to place something; lie is to recline.
• Livid/Lurid: Livid is furiously angry; lurid is shockingly vivid.
• Loose/Lose: Loose is not tight; lose is to misplace.
• Loathe/Loath/Loth: Loathe means to hate; loath/loth means unwilling.
• Luxuriant/Luxurious: Luxuriant means abundant; luxurious means opulent.
• Macho/Manly: Macho is aggressively masculine; manly is having traditionally masculine qualities.
• Majority/Most of: Majority is more than half; most of is the greater part.
• Masterful/Masterly: Masterful is domineering; masterly is very skilled.
• May/Might: May indicates possibility or permission; might indicates possibility or past possibility.
• Medical/Medicinal: Medical relates to medicine; medicinal has healing properties.
• Metal/Mettle: Metal is a substance; mettle is courage.
• Meter/Metre: Meter is a measuring device; metre is a unit of length.
• More Than/Over: More than is for quantities; over is for spatial relationships.
• Naked/Nude: Naked is without clothes; nude is unclothed for artistic purposes.
• Negligent/Negligible: Negligent is careless; negligible is insignificant.
• Niceness/Nicety: Niceness is pleasantness; nicety is a fine detail.
• Objective/Subjective: Objective is unbiased; subjective is based on personal feelings.
• Official/Officious: Official is authorised; officious is overly assertive.
• Ordinance/Ordnance: Ordinance is a law; ordnance is military weapons.
• Peddle/Pedal: Peddle is to sell; pedal is to operate a lever with the foot.
• Perpetrate/Perpetuate: Perpetrate is to commit a crime; perpetuate is to cause to continue.
• Phenomenon/Phenomenal: Phenomenon is an observable fact; phenomenal is extraordinary.
• Pitiful/Pathetic: Pitiful evokes pity; pathetic evokes contempt or pity.
• Populous/Populist: Populous is densely populated; populist appeals to ordinary people.
• Precipitate/Precipitous: Precipitate is to cause to happen suddenly; precipitous is steep or sudden.
• Prevaricate/Procrastinate: Prevaricate is to avoid telling the truth; procrastinate is to delay.
• Quash/Squash: Quash is to suppress; squash is to crush.
• Respective/Irrespective: Respective relates to each individually; irrespective means regardless.
• Restive/Restless: Restive is uneasy or impatient; restless is unable to relax.
• Shall/Will: Shall is used for future with “I” and “we” or to express obligation; will is used for future with other subjects or to express intention.
• Should/Would: Should indicates obligation or expectation; would indicates a conditional or habitual action.
• Simple/Simplistic: Simple is easy or basic; simplistic is overly simplified.
• Stationary/Stationery: Stationary means not moving; stationery is writing materials.
• Suggestible/Suggestive: Suggestible is easily influenced; suggestive implies something indirectly.
• Temerity/Timidity: Temerity is excessive boldness; timidity is lack of courage.
• That/Which: That is used for restrictive clauses; which is used for non-restrictive clauses.
• Tortuous/Torturous: Tortuous is winding or complex; torturous involves severe pain.
• Underlie/Underlay: Underlie is to be the basis of; underlay is to place something under.
• Valuable/Invaluable: Valuable has great worth; invaluable is priceless.
• Who/Whom: Who is a subject pronoun; whom is an object pronoun.
• Who’s/Whose: Who’s is a contraction of “who is”; whose is a possessive pronoun.

Our list is by no means a complete list of homophones but it includes many words commonly misused in journalism. If you want to browse through almost 450 examples you might want to visit Singularis.

Sources used

• Collins English Dictionary and Thesaurus
• The Guide To Better English – Gooden
• The Longman Guide To English Usage – Greenbaum/Whitcut
• The New Fowler’s Modern English Usage – Burchfield
• The Simple Subs Book – Sellers
• A Journalist’s Guide To The Use of English – Bottomley/Loftus

The post Frequently confused words first appeared on Media Helping Media.

A media house needs to be continually assessing whether its output is meeting the needs of the audience and achieving business targets.

This starts in the newsroom and filters through to every area of the organisation.

News is a classic people business: one relatively small set of people passes on information to another larger set of people.

There is a lot of other stuff going on, of course: newsgathering, writing, photography, technology, distribution, business and so on. But in the end it’s one person telling stories to other people.

We know that every human being is different from every other human being and not surprisingly every news boss is different from every other news boss. They all have their own ways of doing things and there is no single formula for success. A lot depends on personality.

But there are some things that are common to every good newsroom and continuous, critical review of its own output is one of them.

In the absence of a formula for the news review process, here are some things a news boss should keep in mind.

• You have to care deeply and passionately about the output. 
  • This means reading, listening or watching everything that comes out of the newsroom and assessing whether it is up to standard.
• You have to convey your enthusiasm to your team.
  • They have to know you are closely interested in all their work. Your enthusiasm will rub off on them, with positive results.
• You set the standards for all to follow.
  • You should work out some simple messages that you keep repeating. For example:
    • The most important quality of every story coming out of this newsroom is accuracy. We don’t accept ANY errors.
    • We all have opinions but when you come to work here, you leave your personal opinions at home.
    • We keep notes on EVERYTHING. We can’t defend a story if you can’t produce your notes.
    • We always try to be fair and straight with everyone, from the subject of our stories to our readers and our colleagues.
    • We don’t just report on a community, we are part of that community and we cannot serve the community unless we understand it intimately
• You never let an error pass unnoticed.
  • This is the single most important element of quality control. If one of your team makes a mistake and nobody mentions it, it means either you didn’t see it or you don’t care. This is demotivating for staff. You must at least talk to them about it.
• Deliver your critical messages wisely.
  • The old days of quality control by means of a fierce telling-off are over. You can be stern and disapproving without bullying. You can also understand that the best journalists are their own fiercest critics. They already feel bad about their error and you don’t need to rub their noses in it. Simply noticing that you are disappointed in them will be enough of a penalty.
• Praise and criticism should be dished out judiciously.
  • We expect a consistently high level of competence so high praise should be reserved for work that is exceptional. Similarly, though, there will always be mistakes, they should be rare and criticism should be proportionate.
• Critical review needs to be systematic, not haphazard.
  • There needs to be a structure, a regular routine of looking back and assessing what has gone right and what has gone wrong. The existence of the routine is a guarantee that important messages will not get overlooked in the pressure of the daily news cycle.
• There should be short-term review meetings.
  • In day-to-day operations, my own preference was to start the morning news meeting with a brief review of what we had done the previous day, before continuing on to plan the day’s coverage. If there was something too sensitive or too important to be raised at that meeting, I would ask the relevant people to stay behind for further discussions.
• There should be long-term review meetings.
  • You need to take a step back, every now and then to look, not just at individual stories, but also at how well you are covering issues. What important themes are you missing? Do you have the right staff to produce the coverage that circumstances require? Are you publishing in the right places and on the right platforms? And so on. I remember attending a meeting in 1975 and my editor suddenly saying: “I’ve realised we did not adequately cover the revolution in Portugal last year.” He explained that if we had thought more carefully about it at the time, we would have spotted the implications of Portugal abandoning its colonies, the result of which was to embolden revolutionary movements throughout Southern Africa.
• Include the competition in your reviews.
  • You should not assess your organisation’s performance in isolation.It’s no good if you did a pretty good job but your competitors did a better one. Did they have stories you missed? Were they first with the news? Did they think more creatively about how to handle a particular story? Were their headlines better? Always tell your own staff that you will never accept being anything less than number one in your market.
• You need to identify and understand your audience.
  • A great British newspaper editor in the middle of the 20th Century told his staff they were writing for “the man on the Clapham omnibus and the woman on Rhyl promenade.” He went on to say these imaginary folk represented ordinary people who were interested in improving their lives, owning their own homes and getting on in the world. This is similar to the modern technique of audience segmentation. It helps you to know what kind of stories your audience is interested in, and where they go to get their information. You cannot have too much information about your audience.
• A news organisation that goes broke is no use to anyone.
  • The news media is frequently under financial pressure and many titles have disappeared in the past 20 years. The editorial output of the news must be consistent with a viable and sustainable business strategy. So there should be regular reviews, with the relevant business colleagues, about how the news operation can play a part, within its existing ethical and editorial standards, in ensuring the success of the business.
• Review your own performance.
  • You expect your staff to be self-critical and you should do the same. At the end of every day, ask yourself: did I do everything I could to make today’s output as good as possible? If not, did I miss any obvious chances? Did I deal with everybody fairly and professionally? Did I inject a bit of fun into proceedings, to alleviate tension and make everybody feel better?

Bob Eggington

Related articles

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How to run an effective news meeting

Getting the best out of a news meeting

Hey AI, what are the top stories of the day?

The post Reviewing news output is essential first appeared on Media Helping Media.

By diversity we are talking about a wide range of identities, including but not limited to race, ethnicity, gender, sexual orientation, disability, socioeconomic background, and religion.

In an article on this site, Naomi Goldsmith wrote about the role of women in the media. In ‘Gender equality in the media’ she argued that “Gender equality is important for both men and women. Neither gender benefits from being stereotyped and not being allowed to fulfil its full potential.”

Gender is just one area covered by diversity. The following are some of the ethical, editorial and business reasons why diversity in both staffing and coverage is important:

1. Accurate and authentic representation:
   • Diverse staffing:
     • Newsrooms and media organisations with diverse staff are better equipped to understand and report on the complexities of diverse communities.
     • Individuals from different backgrounds bring perspectives, experiences, and cultural knowledge that can enrich storytelling.
     • Employing journalists from a mixture of backgrounds helps avoid stereotypical or one-dimensional portrayals of marginalised groups.
   • Diverse coverage:
     • When media outlets prioritise diverse coverage, they give voice to underrepresented communities and shed light on issues that might otherwise be ignored.
     • This fosters a more inclusive and accurate reflection of society, promoting understanding and empathy while also creating richer editorial output..
2. Countering bias and promoting fairness:
   • Diverse staffing:
     • A lack of diversity in media can lead to unconscious bias in reporting, resulting in skewed or inaccurate story telling.
     • Diverse newsrooms are more likely to challenge these biases and ensure fair and accurate coverage – while taking care to avoid false equivalence and false balance.
   • Diverse coverage:
     • Media that consistently prioritises diverse viewpoints helps to counter dominant narratives and challenge prevailing assumptions.
     • This promotes a more comprehensive understanding of complex social issues.
3. Building trust and credibility:
   • Diverse staffing:
     • When audiences see themselves reflected in the media, they are more likely to trust the information they receive.
     • Diverse newsrooms foster a sense of inclusivity and build stronger connections with their communities.
   • Diverse coverage:
     • Media that accurately and respectfully portrays diverse communities gains credibility and fosters trust with a wider audience.
     • This is essential for maintaining a healthy and informed public discourse.
4. Enhancing creativity and innovation:
   • Diverse staffing:
     • Diverse teams bring a wider range of perspectives and ideas, leading to more creative and innovative storytelling.
     • This can help media organizations stay relevant and engage with a rapidly changing audience.
   • Diverse coverage:
     • By highlighting diverse stories, media outlets expose their audiences to new perspectives and ideas, enriching the cultural landscape.

Supporting information:

• National Union of Journalists (NUJ):
  • The UK’s NUJ highlights what it sees as a concerning lack of diversity in UK newsrooms, emphasising the need for greater representation.
  • Link: Fewer than one in five media workers believe newsroom diversity is a top priority in their workplace – National Union of Journalists (NUJ)
• Jefferson University:
  • The importance of representation is underlined by information from Jefferson University which shows how media can shape perspectives.
  • Link: How can the media Improve representations of diverse identities?
• The Diversity Dashboard:
  • The importance of diversity in media careers is set out by the UK’s Diversity Dashboard. The site contains information diversity in the media industry along with the many different career paths within the media and journalism.
  • Link: Careers in media: Opportunities and the importance of diversity

In conclusion, diversity in media is not just a matter of social justice; it is essential for producing accurate, fair, and engaging content that reflects the complexity of our world.

Diversity in journalism is crucial not only for fair representation but also for improving the quality and relevance of news coverage. It ensures that media content speaks to a broader audience, reflecting the complexity of the society it serves.

Diversity encompasses a wide range of identities, including race, ethnicity, gender, sexual orientation, disability, socioeconomic background, and religion. By including these varied perspectives, journalism becomes more comprehensive, nuanced, and relatable.

One key area where diversity matters is in staffing. Journalists from different backgrounds bring unique experiences, cultural knowledge, and perspectives that enrich storytelling and lead to more authentic representation. This helps avoid stereotypical portrayals, particularly of marginalised groups, while providing greater depth and complexity in news coverage. A diverse newsroom is also better equipped to understand and report on communities often overlooked by traditional media.

Ensuring diversity in coverage is equally important. By reporting on a wide range of voices and experiences, media outlets shine a light on underrepresented communities and the issues that affect them. This approach not only promotes empathy and understanding but also helps counteract bias and challenge dominant narratives. A diverse editorial focus allows journalism to tackle complex social issues in a way that is fair, accurate, and informative.

Diversity also plays a critical role in building trust and credibility. Audiences are more likely to engage with a media organisation that reflects their realities and perspectives. When people see themselves accurately and respectfully portrayed, it strengthens their connection to the news and fosters a more inclusive media landscape. This is essential for maintaining public trust and supporting healthy democratic discourse.

Lastly, diversity enhances creativity and innovation within journalism. A wide range of voices in the newsroom leads to more dynamic storytelling, fresh ideas, and new ways of engaging audiences. Similarly, diverse coverage exposes audiences to different cultures, experiences, and viewpoints, enriching the overall media landscape.

In an increasingly diverse and global society, prioritising diversity in journalism is not just ethically important, it is vital for the industry’s relevance and survival.

The post Diversity in journalism first appeared on Media Helping Media.

The output of a news organisation is determined by how well the newsroom is run. A well-managed newsroom is more likely to produce compelling and engaging content focused on audience need.

How the editor manages their team is crucial. In previous articles we have looked at how to run a news meeting and the elements that are required. We also looked at what part AI (artificial intelligence) could play if invited to take part. Here we look at the strategic considerations.

Newsrooms are continually evolving, and, as a result, so are newsroom cultures, practices, and meetings. The development of electronic newsgathering, the internet, multimedia journalism, social media, data journalism have changed the dynamic. Here we look at how newsroom management needs to develop.

1. Cultivating a culture of insight and impact:
   • As well as focusing on ‘original stories’, emphasise the importance of insightful analysis, contextualisation, and impact-driven reporting. In today’s saturated news environment, simply breaking a story isn’t enough. Readers need to understand why it matters. This involves digging deeper into a story to find important news angles.
   • Encourage staff to ask: “How does this affect our audience?” “What are the long-term implications?”
   • Example: Instead of just reporting on a new policy, delve into its potential consequences for specific communities, backed by data and expert opinions.
2. Strategic story selection and prioritisation:
   • Introduce a framework for evaluating story ideas based on:
     • Audience relevance: Who cares about this story, and why?
     • Impact: What is the potential impact of this story?
     • Exclusivity: How unique is our angle?
     • Feasibility: Can we realistically deliver this story with our resources?
     • Mission: Does this story reflect the values and focus of the publication?
     • News value: Apply a Story weighting system for assessing news value
     • Content value: Check the story’s value to your news organisation and audience by using the Content value matrix developed by Media Helping Media.
3. Data-driven journalism and digital integration:
   • Incorporate the importance of data journalism, digital storytelling, and audience engagement.
   • Encourage staff to:
     • Use data to uncover trends and patterns.
     • Explore interactive visualisations and multimedia formats.
     • Engage with audiences on social media and online platforms.
     • Analyse web traffic and social media analytics to see what stories are performing well, and why.
   • This will allow for a more modern approach to journalism.
4. Foster a culture of innovation and experimentation:
   • Go beyond “original ideas” to encourage innovative storytelling techniques and formats.
   • Introduce regular “innovation sessions” where staff can pitch and develop experimental projects.
   • Embrace a “fail fast, learn faster” mentality.
   • Allow for a small percentage of time to be spent on experimental stories.
5. Ethical considerations and responsible reporting:
   • Emphasise the importance of ethical journalism, accuracy, and fairness.
   • Discuss potential ethical dilemmas and how to navigate them.
   • Promote responsible reporting practices, especially in sensitive situations.
   • Discuss the importance of fact checking, and multiple source verification.
6. Mental health and well-being:
   • Journalism can be a stressful profession. Acknowledge the importance of mental health and well-being.
   • Encourage open communication about stress and burnout.
   • Promote a healthy work-life balance.
   • Make sure that staff know that they are supported.
7. Diversity and inclusion:
   • Ensure that the newsroom and the stories that are covered reflect the diversity of the community.
   • Ensure you have gender equality in terms of the news organisation and its output.
   • Encourage diverse perspectives and voices.
   • Avoid stereotypes and promote inclusive language.
   • Make sure that the newsroom is a safe and welcoming place for everyone.
8. Training and development:
   • Provide ongoing training and development opportunities for staff.
   • Keep up with the latest trends and technologies in journalism.
   • Encourage staff to attend conferences and workshops.
   • Create a culture of continuous learning.

Summary

• The editor: The editor’s role is not just to generate enthusiasm but to be a strategic leader, mentor, and facilitator.
• Collaboration: Encourage a collaborative environment where ideas are shared, challenged, and refined.
• Embracing change: The media landscape is constantly evolving. A successful newsroom must be adaptable and willing to embrace change.

By incorporating these suggestions an editor can create a dynamic, impactful, and sustainable news operation.

To add more value, perspective, and depth to the discussion of newsroom management, we need to consider the broader context of journalism’s role in society and the challenges it faces. Here’s an enhanced perspective:

• The evolving role of the editor:
  • Beyond mere content production, the modern editor must act as a steward of public trust. This involves not only ensuring factual accuracy but also fostering a nuanced understanding of complex issues. In an era of misinformation, the editor’s role in promoting media literacy and critical thinking becomes paramount.
  • This requires a shift from a purely reactive news cycle to a proactive, context-driven approach. Editors must encourage their teams to “connect the dots,” providing audiences with the necessary background and analysis to make informed decisions.
• The ethical of digital journalism:
  • The rapid proliferation of digital platforms has created an ethical challenge for journalists. Issues such as algorithmic bias, data privacy, and the spread of deepfakes demand careful consideration.
  • Newsrooms must develop robust ethical frameworks that address these emerging challenges, fostering a culture of responsible innovation. This involves ongoing dialogue about the potential consequences of new technologies and a commitment to transparency in reporting.
• Inclusive storytelling:
  • In an increasingly fragmented society, newsrooms have a responsibility to amplify diverse voices and perspectives. This goes beyond simply “checking boxes” to ensure representation.
  • True inclusion requires a deep understanding of the communities being served, fostering authentic relationships, and challenging ingrained biases. Editors must cultivate newsrooms where diverse backgrounds and experiences are valued, leading to more nuanced and accurate reporting.
• Cultivating resilience:
  • The journalism industry faces unprecedented economic and technological disruption. Newsrooms must adapt to these challenges by embracing innovative business models and fostering a culture of resilience.
  • This involves investing in training and development, encouraging experimentation, and creating a supportive environment where journalists can thrive. Editors should focus on building organisations that are adaptable, and able to learn and change quickly.
• Mental health in a high-stress profession:
  • Journalism is a high stress profession, and it is becoming more so. The constant flow of information, and the often traumatic events that are covered, take a toll on journalists. Editors must understand and provide help for journalists experiencing stress and trauma in the course of their work.
  • News organisations have a duty of care to their employees, and must promote a culture of mental well being. This includes promoting healthy work life balances, and making sure that employees know that they are supported.

By considering these broader perspectives, newsrooms can move beyond simply producing content to fulfilling their essential role in a healthy democracy.

The post Creating a vibrant newsroom culture first appeared on Media Helping Media.

I can remember when I first realised I was a data journalist, or at least helping to produce data journalism.

It was in the summer of 1997 when we were getting ready to launch the BBC News website.  (And by the way, I don’t get any marks for being perceptive, because as we point out in the article ‘What is data journalism?‘, all journalists are data journalists, whether they know it or not, so I had been one since the 1960s.)

Anyway, we were looking at how to produce and improve news stories – and all our assumptions belonged in the analogue age.

We were obviously aware that unlike television and radio, online news was not an ephemeral, one-word-at-a-time medium. Users could dwell on text and be directed to other information for valuable context and background.

We wanted to offer rich, instantly-available material that supplemented and enhanced every story.

But to produce that kind of material, we were used to relying on our own and our colleagues’ memories and archives, the BBC’s tape and audio libraries, a newspaper cuttings library and rudimentary newsroom systems that were not connected to the Internet.

In other words, it was a bit haphazard, almost certainly incomplete, relied on a lot of legwork and took ages.

Suddenly, as our tech guru patiently explained to us, we had electronic access to all kinds of valuable material.  He called it “data”.  The penny dropped.

We could automatically link to related stories. We could use search to produce the raw data for time-lines and fact files.  We could pull down stories being written on primitive terminals in the BBC’s  Moscow newsroom and automatically format them as web pages.

We even had a stab at a bit of software that would automatically create a timeline on important, recurring stories. It would search all our sources for, say, unrest in any particular country and produce a list of events.

To make the list usable, we had to instruct it not to put any two items too close together chronologically, unless they were very important, and to exclude items of lesser importance if the list was too long.

It was very ambitious and I cannot remember if we ever got round to implementing this functionality.  If we did, then we almost invented an early version of artificial intelligence.

But now, the real thing is here, and the new capabilities that fascinated and thrilled us in those early years are now easily and freely available to everyone, in much more powerful versions, thanks to the power of large language models, neural networks and immense distributed computing power.

So now, not only are all journalists data journalists, we all have access to immense quantities of priceless data and the tools to make good use of it.  We have listed many of those data tools and resources.

They are wonderful.  But do not forget that in the term “data journalist” the second word is more important than the first.

We should all be thrilled and grateful for the things Artificial Intelligence makes possible, but the most powerful tools are still the human journalist’s instinct, judgement and training.

Bob Eggington

This text offers a fascinating glimpse into the nascent stages of digital journalism, particularly the moment when the author recognised the inherent data-driven nature of the craft. Let’s expand on this, adding depth, meaning, and perspective:

The ubiquity of data journalism 

The assertion that “we are all data journalists” transcends a mere label. It’s a fundamental recognition of the information age’s defining characteristic: the sheer volume of data surrounding us. Even before the term gained currency, journalists were implicitly engaged in data analysis, sifting through facts, statistics, and records to construct narratives. The shift, as the author articulates, lies in the accessibility and utility of data.

The analogue to digital leap

The author’s recollection of the BBC News website’s launch in 1997 is a powerful illustration of this transition. The limitations of analogue methods – reliance on memory, physical archives, and disconnected systems – highlight the transformative potential of digital data. The “tech guru’s” revelation wasn’t just about accessing “valuable material”; it was about recognising the inherent structure and relationships within information, the ability to connect disparate pieces into a coherent whole.

Beyond automation

The ambitious attempt to create an automated timeline generator speaks to the early recognition of AI’s potential in journalism. The challenges faced – managing chronological proximity and prioritising information – are precisely the problems that modern AI and machine learning algorithms address. This anecdote is more than a historical footnote; it’s a testament to the foresight of those who recognised the need for intelligent data processing.

The democratisation of data and tools

The author rightly points out that the tools that were once the exclusive domain of tech-savvy journalists are now widely accessible. Large language models, neural networks, and distributed computing have democratised data analysis, empowering individuals to explore, interpret, and visualise information in unprecedented ways. This democratisation, however, does not diminish the importance of journalistic ethics and skills.

The enduring significance of the journalist

The emphasis on “journalist” over “data” is crucial. While AI can automate tasks and provide insights, it cannot replace the human element of journalism. The author’s “instinct, judgement and training” remain indispensable. This encompasses:

• Critical thinking: Evaluating the credibility and relevance of data sources.
• Contextualisation: Placing data within a broader social, political, and historical framework.
• Ethical considerations: Recognising and mitigating biases in data and algorithms.
• Narrative construction: Crafting compelling stories that resonate with audiences.
• Human empathy: Understanding and conveying the human impact of data-driven insights.
• Accountability: Holding power to account, even when the power is expressed in data.

The evolving role of the data journalist

The modern data journalist is not merely a data wrangler but a storyteller, an investigator, and a communicator. They must possess a blend of technical skills and journalistic acumen. They must be able to:

• Extract meaningful insights from complex datasets.
• Visualise data in a clear and engaging manner.
• Communicate data-driven findings to diverse audiences.
• Understand the limitations and biases of data and algorithms.
• Use data to uncover hidden patterns and trends.

A call for responsible innovation

As AI continues to transform journalism, it is essential to remember that technology is a tool, not a replacement for human intelligence. The focus should be on using AI to enhance journalistic capabilities, not to automate them entirely. The ethical implications of AI in journalism – including issues of bias, transparency, and accountability – must be carefully considered.

In conclusion, the author’s reflections provide a valuable perspective on the evolution of data journalism. The journey from analogue limitations to digital possibilities underscores the transformative power of data. However, the enduring importance of journalistic integrity and human judgment reminds us that technology is only as good as the people who use it.

Related articles

Data journalism – resources and tools

Data journalism glossary

What is data journalism?

The post Good journalism has always been about data first appeared on Media Helping Media.

We have compiled a list of some of the leading resources and tools that are available for those starting out in data journalism.

This list will be updated over time. You might want to consult our Data journalism glossary to look up some of the terms that appear below.

Tools;

Below is a list of tools used by data journalists. They cover data gathering, cleaning, analysis, and visualisation. These tools are great for both beginners and experienced data journalists:

Data collection & scraping tools

• Tabula: (Extract tables from PDFs) https://tabula.technology/
• Web scraper Chrome extension: (Browser-based web scraping tool) https://www.webscraper.io/
• Google Sheets: ImportXML, ImportHTML, and ImportData functions are powerful for scraping web data. https://www.google.com/sheets/about/
• Wayback Machine: For finding historical web data or removed pages https://archive.org/web/

Data cleaning & preparation

• OpenRefine: Powerful tool for cleaning messy datasets and transforming formats. https://openrefine.org/
• Trifacta Wrangler (Cloud free version): Data cleaning and shaping tool (registration required) https://www.trifacta.com/products/wrangler/
• Open Data Institute Labs: Validate CSV files for errors and formatting issues. https://csvlint.io/

Data visualisation tools

• Datawrapper: Create charts, maps, and tables easily. https://www.datawrapper.de/
• Flourish: Interactive graphics and storytelling visuals. https://flourish.studio/
• Chartbuilder: (Quartz’s open-source chart tool) https://github.com/Quartz/Chartbuilder
• RawGraphs: Great for creating complex data visualisations (exports as SVG). https://app.rawgraphs.io/
• Tableau Public: A powerful data visualisation tool that allows you to create interactive dashboards and visualisations. The public version is free for public data. https://www.tableau.com/solutions/web-data-sources

Mapping tools

• Google My Maps: Simple mapping tool for plotting points or routes. https://www.google.com/maps/d/
• Kepler.gl: Advanced geospatial analysis and visualisations. https://kepler.gl/
• QGIS (Free, open-source GIS software): Desktop software for serious mapping projects. https://qgis.org/en/site/

Data analysis & statistics tools

• Google Sheets: Built-in analysis, pivot tables, and scripting. https://www.google.com/sheets/about/
• Python (with pandas, matplotlib, seaborn): Free programming tools for serious data analysis. https://www.python.org/
• R Project (Statistical computing and graphics): https://www.r-project.org/

Fact-checking & verification tools

• TinEye: Reverse image search for verifying photos. https://tineye.com/
• Google Fact Check Explorer: Search verified fact-checks globally. https://toolbox.google.com/factcheck/explorer

Other handy tools

• TimelineJS: Create interactive timelines. https://timeline.knightlab.com/
• StoryMapJS: Tell stories with maps and multimedia. https://storymap.knightlab.com/
• Otter.ai (Free tier for transcription): https://otter.ai/
• Excalidraw: Sketch quick diagrams or workflows. https://excalidraw.com/
• Jupyter Notebooks: An open-source web application that allows you to create and share documents that contain live code, equations,  visualisations, and narrative text. https://jupyter.org/

Tools for specialist reporters and correspondents

• Climate, environment & energy
  • Global Forest Watch: Deforestation and land-use data, interactive maps https://www.globalforestwatch.org/
  • NASA Earthdata: Access satellite imagery, climate, and environmental datasets. https://earthdata.nasa.gov/
  • Carbon Brief: Data-driven analysis on climate science and energy policy. https://www.carbonbrief.org/
  • Climate Data Online (NOAA): U.S. climate data including historical weather records. https://www.ncdc.noaa.gov/cdo-web/
• Health, medicine & pandemics: 
  • World Health Organization (WHO) Data: Global health statistics and disease surveillance. https://www.who.int/data
  • Our World in Data (OWID): COVID-19, vaccines, and global health datasets. https://ourworldindata.org/
  • CDC Data & Stats: U.S. public health datasets including mortality, diseases, and surveillance. https://data.cdc.gov/
  • IHME Global Health Data: Health metrics and disease burden visualisations. https://www.healthdata.org/
• Finance, business & economy
  • U.S. Securities and Exchange Commission (SEC) EDGAR Database: Corporate filings, financial reports, executive compensation. https://www.sec.gov/edgar.shtml
  • World Bank Open Data: Economic and development datasets globally. https://data.worldbank.org/
  • IMF Data Portal: Macroeconomic and financial data from the International Monetary Fund. https://data.imf.org/
  • OpenCorporates: Global corporate registries and company data. https://opencorporates.com/
• Crime, justice & human rights
  • Bureau of Justice Statistics (U.S.): Criminal justice datasets, prison data, and law enforcement stats. https://bjs.ojp.gov/
  • UN Office on Drugs and Crime (UNODC): Global crime, drug, and human trafficking data. https://dataunodc.un.org/
  • The Marshall Project – Data Resources: Journalism and data on criminal justice in the U.S. https://www.themarshallproject.org/
• Politics, elections & government transparency
  • Google Public Data Explorer: Visualisation of world development, economy, and politics datasets. https://www.google.com/publicdata/directory
  • U.S. Census Bureau: Demographic, housing, and political district data. https://data.census.gov/
  • OpenSecrets: U.S. campaign finance, lobbying, and political donation tracking. https://www.opensecrets.org/
  • VoteSmart: Track political candidates, voting records, and campaign donors. https://justfacts.votesmart.org/
• General data search & repositories
  • Google Dataset Search: Search datasets across thousands of open data portals. https://datasetsearch.research.google.com/
  • Kaggle Datasets: Public datasets for machine learning, data journalism, and analysis. https://www.kaggle.com/datasets
  • U.S. Data.gov: Access to open datasets from U.S. government agencies. https://www.data.gov/
  • Humanitarian Data Exchange (HDX): Global crisis and humanitarian data. https://data.humdata.org/

Considerations for using free tools:

• Data privacy: Be mindful of data privacy when using free tools, especially when working with sensitive information.
• Learning curves: Some powerful free tools might have a steeper learning curve than paid alternatives.
• Community support: Look for tools with active communities, as this can provide valuable support and resources.

By combining these free resources, you can build a strong foundation in data journalism without breaking the bank.

Websites:

• The Data Journalism Handbook / European Journalism Centre (EJC)
  • Website: https://datajournalism.com/
• International Consortium of Investigative Journalists (ICIJ)
  • Website: https://www.icij.org/
• The Center for Investigative Reporting / Reveal
  • Website: https://revealnews.org/
• The Investigative Reporters and Editors (IRE)
  • Website: https://www.ire.org/
• Global Investigative Journalism Network (GIJN)
  • Website: https://gijn.org/
• Reuters Institute for the Study of Journalism (RISJ):
  • Website: https://reutersinstitute.politics.ox.ac.uk/
• Pew Research Center:
  • Website: https://www.pewresearch.org/
• Columbia Journalism School – Tow Center for Digital Journalism:
  • Website: https://journalism.columbia.edu/
• Bellingcat:
  • Website: https://www.bellingcat.com/
• Datawrapper Academy
  • Website: https://blog.datawrapper.de/
• Knight Center for Journalism in the Americas
  • Website: https://journalismcourses.org/
• R Journalism (Subreddit for Data Journalism)
  • Website: https://www.reddit.com/r/DataJournalism/
• OpenNews
  • Website: https://opennews.org/

Related articles

Good journalism has always been about data

Data journalism glossary

What is data journalism?

The post Data journalism – resources and tools first appeared on Media Helping Media.

Often used words and phrases

• Algorithm:
  • A set of rules or instructions that a computer follows to solve a problem or perform a task. In data journalism, algorithms can be used for various purposes. Link: Algorithm
• API (Application Programming Interface):
  • A digital tool that lets you pull data directly from a website or database, often used by journalists to access updated datasets. Link: API
• Choropleth map:
  • A map shaded in different colours to show how a number or rate changes by area (e.g., COVID-19 cases by county). Link: Choropleth map
• Computational thinking:
  • The process of breaking down complex problems into smaller, manageable parts, and then creating algorithms to solve them. Link: Computational thinking
• Correlation:
  • A relationship between two variables (note: correlation doesn’t mean causation). Link: Correlation
• CSV (Comma-Separated Values):
  • A common, simple file format for datasets which is basically a spreadsheet saved as plain text. Link: CSV
• Data analysis:
  • Examining data to identify trends, patterns, and relationships. Link: Data analysis
• Data bias:
  • When data is skewed or incomplete journalists need to be alert to this to avoid misleading the audience. Link: Data bias
• Data cleansing (or wrangling):
  • The process of fixing messy data in order to correct errors, fill in missing info, and format it so it’s ready for analysis. Link: Data cleansing
• Data ethics:
  • Principles and guidelines for the responsible collection, analysis, and dissemination of data, with a focus on privacy, security, and fairness. Link: Data ethics
• Data journalism:
  • The practice of using data to find, create, and tell news stories. It involves collecting, analysing, and visualising data to inform the public. Link: Data journalism
• Data leak (or breach):
  • When private or sensitive data is released, intentionally or accidentally, newsrooms often investigate these. Link: Data leak or breach
• Data literacy:
  • The ability to understand, interpret, and communicate data effectively. This includes critical thinking, statistical reasoning, and the ability to identify biases. Link: Data literacy
• Data mining:
  • The process of extracting valuable information and patterns from large datasets. Link: Data mining
• Data scraping:
  • Data scraping is the automated process of extracting data from websites or other sources and saving it into a structured format. Link: Data scraping
• Data transparency:
  • Being open about how the data was handled, what assumptions were made, and what might be missing.
• Data visualisation:
  • Representing data visually through charts, graphs, maps, and other graphical formats. Link: Data visualisation
• Dataset:
  • Or data-set is a collection of related data, like a spreadsheet or table, often the starting point for a data story. Link: Dataset
• Deduplication:
  • Removing repeated entries in a dataset to avoid counting the same thing twice. Link: Data deduplication
• Descriptive statistics:
  • Simple summaries of data, such as averages, medians, and percentages, that help explain your findings. Link: Descriptive statistics
• FOIA (Freedom of Information Act) Request:
  • A formal request for public records from a government agency which is used to obtain datasets not posted online. Link: Freedom of Information Act (by country)
• Geospatial data:
  • Data that includes location information which is essential for making maps or analysing patterns by area. Link: Geospatial data
• Heat map:
  • A graphic that uses colour intensity to show concentrations of activity or numbers. Link: Heat map
• Interactive graphics:
  • Visuals that let readers explore data such as maps you can zoom in on or filters to compare regions.
• Interactive visualisation:
  • Visualisations that allow users to explore and interact with the data. Link: Interactive visualisation
• JSON (JavaScript Object Notation):
  • A format often used by websites and APIs to structure data. Journalists may need to convert this into tables. Link: JSON
• Machine learning:
  • Computer systems analysing data to find patterns. Used in investigative journalism for things like identifying fake accounts. Link: Machine learning
• Margin of error:
  • A measure of how much uncertainty there is in survey results. This is particularly important when reporting on political opinion polls. Link: Margin of error
• Natural Language Processing (NLP):
  • A way to automatically analyse large amounts of text such as searching through thousands of documents for themes. Link: NLP
• Normalisation:
  • Adjusting numbers to make fair comparisons such as calculating rates per 100,000 people instead of raw numbers. Link: Normalisation
• Open data:
  • Data published by governments, organisations, or researchers that’s free for anyone to use in their reporting. Link: Open data
• Outlier:
  • A data point that sticks out because it’s much higher or lower than the rest. Sometimes these lead to important news stories. Link: Outlier
• Parsing:
  • Breaking down complex information (such as addresses or dates) into standardised parts for easier analysis. Link: Parsing
• Regression analysis:
  • A more advanced statistical method to explore relationships between variables. This is sometimes used in deep journalistic investigations. Link: Regression analysis
• Sampling bias:
  • This exists when the group surveyed or studied doesn’t represent the larger population. This can distort results and conclusions. Link: Sampling bias
• SQL (Structured Query Language):
  • A coding language for searching through large databases. This is helpful for investigative journalism projects. Link: SQL
• Spreadsheet:
  • A basic tool such as Excel or Google Sheets that most journalists use to store, sort, and analyse data. Link: Spreadsheet
• Statistical analysis:
  • Using statistical methods to analyse data, including things such as finding the mean, median, and mode, and also finding standard deviations. Link: Statistical and data analysis
• Structured data:
  • Data organised in rows and columns (such as Excel spreadsheets) that’s easy to sort and analyse. Link: Structured data analysis
• Time series data:
  • Data collected over time. This is useful for spotting trends, such as changes in crime rates or housing prices. Link: Time series database
• Tooltip:
  • A small pop-up box in a graphic that appears when readers hover over a data point to reveal details. Link: Tooltip
• Unstructured data:
  • Data that doesn’t come in neat tables, such as PDFs, social media posts, or interview transcripts. Link: Unstructured data
• Web scraping:
  • The process of automatically extracting data from websites. Link: Web scrapin

Related articles

Data journalism – resources and tools

What is data journalism?

Good journalism has always been about data

The post Data journalism glossary first appeared on Media Helping Media.