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.

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.

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

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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.

Data journalism, also known as data-driven journalism, is the process of finding, understanding, and processing information in order to produce news stories.

It’s always been part of the news production workflow but has increased in importance since the development of computers and the internet.

In the past journalists used to analyse numbers by hand trying to make sense of what they had jotted down in their notebooks when out covering a story.

By just asking the basic journalistic questions of what, why, when, how, where, and who, journalists were gathering data. This would result in collecting important data such as:

• What has happened?
  • Event type and frequency: Crash, fire, riot – is it this the first time, the 10th time – how many times?
• How many people were affected?
  • Number of people killed or injured, ambulances, police deployed
• When did this happen?
  • Time and date, rush hour, drive time, overnight, morning.
• Where did it happen?
  • Location – street, town, intersection, map reference, accident blackspot, area of known tension perhaps
• Who could have more information?
  • Local authority or police records, facts and figures regarding similar events in the past.

In the example above the reporter would have jotted down any information they could find about the story they were covering. Those notes contained data which would be an essential part in telling the story.

That data, if processed and then analysed, could help the journalist and their team dig much deeper. But there was limited access to that data.

It would be contained in the reporter’s notebook, in the next edition of the newspaper, or broadcast in the next news bulletin, and stored in a newsroom archive as a physical cutting – but it would be hard to retrieve or be of much further use. (See – The importance of keeping records)

Perhaps a diligent journalist, who was specialising in a particular area, or working on an investigation, would create a simple hand-drawn spreadsheet to try to crunch the numbers, but often they were soon sent off to cover the next story and the data they had gathered would be put to one side.

Then came computers. This enabled journalists to store data and make sense of it using spreadsheets to look for patterns in terms of frequency, size, time, and any relationships between events.

With the development of the internet it became easier to find and share large amounts of data. Computers could be used to connect the data in ways that would have been impossible for a journalist in the past.

This resulted in computer assisted reporting (CAR) which uses technology to analyse data and helps journalists find hidden stories and investigate complex issues such fraud and corruption.

By examining large datasets – structured collections of related data revealing patterns, trends, and relationships –  journalists are able to produce more accurate and impactful journalism.

Computers also enable journalists to display the data they had gathered in graphs, charts, and maps – this is called data visualisation – which means that complex datasets can be displayed in easy to understand ways.

Data journalism is now an important part of news production with many journalists using advanced tools to find complex stories. And they are able to share their data so everyone can see where the information came from. This also leads to collaboration between different teams of journalists working together on a complex and important investigation.

In summary, data journalism has progressed from being a specialist practice, to an integral part of modern news reporting in several ways:

• Data analysis: Collecting, organising, and examining large amounts of data to uncover trends, patterns, and news angles.
• Storytelling: Using the insights uncovered to create compelling and informative news stories, and presenting complex information in a clear and easy to understand way.
• Visualisation: Creating charts, graphs, and maps to help audiences understand the stories behind the data.
• Tools: The use of spreadsheets, statistical software, and data visualisation platforms to process data in order to make it more useful in the news production process.
• Evidence: By including reliable and rich data in stories, data journalism can provide a more objective and evidence-based approach to reporting.
• Quantity: Data journalism enables a journalist to sift through large amounts of data – such as survey results, financial figures, football results, and government records to find stories hidden within that data.
• Accessibility: The journalist can then present those stories in a clear and easy-to-understand way using charts and graphs.
• Reliability: Instead of just relying on someone’s opinion, as has often been the case in the past, the journalist can use facts and figures to back up their reporting.

Data journalism – further thoughts

Journalism has always been a pursuit of truth, sifting through the noise to reveal what matters. At its core lies the fundamental task of gathering, analysing, and presenting information in ways that help society make sense of the world.

Over time, the methods used by journalists have evolved, but one constant remains: data has always been central to storytelling, whether jotted in a notebook or embedded within sprawling digital databases.

What has changed dramatically is the scale, speed, and sophistication with which journalists can access and interrogate information. The digital age has transformed raw data from fragmented observations into powerful tools for accountability, insight, and public understanding.

Where once reporters might have tallied casualty figures by hand or kept mental notes on patterns they noticed over time, they now wield vast datasets – crime records, health statistics, financial disclosures, social media activity – as both sources and subjects of their investigations.

The shift is not merely technological but philosophical. Data-driven journalism reframes the journalist’s role. They are no longer just a chronicler of events, they are also an investigator uncovering patterns invisible to the naked eye.

A single incident becomes part of a larger puzzle: a crash is not just an accident but potentially a symptom of systemic infrastructure failures; a spike in evictions reveals deeper housing inequities; electoral results expose demographic shifts and political realignments.

Data breathes life into these stories, adding context, nuance, and evidence that deepens public understanding.

With computational tools, journalists move beyond surface narratives to probe the why and how, not just the what. Algorithms, spreadsheets, and statistical models allow them to test hypotheses, verify claims, and uncover hidden relationships.

This capability becomes crucial in an era where misinformation spreads fast, and complex issues, such as climate change, global pandemics, economic inequality, demand rigorous scrutiny.

Equally transformative is the way data enables storytelling. Visualisations such as maps, charts, interactive graphics, help translate complexity into clarity. They allow audiences to see the scale of a crisis, the trajectory of a trend, or the impact of policy decisions in ways that words alone cannot achieve.

Good data visualisation doesn’t just display numbers; it creates an emotional and intellectual connection, turning abstract figures into human stories.

Another profound shift is the collaborative nature of modern data journalism. No longer confined to individual reporters. Many of the most impactful investigations today involve teams of journalists, data scientists, designers, and programmers working together across borders.

Global projects such as the Panama Papers or investigations into environmental destruction exemplify the power of shared datasets and collaborative analysis. Transparency in these projects – publishing methodologies, sharing datasets – also strengthens trust in journalism at a time when skepticism is high.

Ultimately, data journalism enriches the very purpose of the media: to inform, to explain, and to hold power to account. By grounding stories in verifiable evidence, it elevates reporting from anecdote to analysis, offering audiences not just opinions but actionable insights.

As data becomes ever more abundant, the journalist’s challenge is to remain not just a transmitter of information, but a skilled interpreter – someone who can connect the dots, surface the hidden stories, and empower the public to see the world more clearly.

Data is no longer a byproduct of reporting; it is a fundamental driver of journalism’s future.

Questions and Answers

1. Question: What is data journalism, and how has its importance changed over time?
   • Answer: Data journalism, also known as data-driven journalism, is the process of finding, understanding, and processing information to produce news stories. While it has always been a part of news production, its importance has significantly increased with the development of computers and the internet, allowing for more efficient and in-depth analysis of large datasets.
2. Question: How did journalists gather and analyse data before the widespread use of computers?
   • Answer: Before computers, journalists gathered data by hand, jotting down notes in notebooks and attempting to analyse them manually. They used basic journalistic questions such as “what,” “why,” “when,” “how,” “where,” and “who” to collect information. Sometimes, diligent journalists would create hand-drawn spreadsheets for simple analysis, but this was often time-consuming and limited.
3. Question: What is Computer Assisted Reporting (CAR), and how has it transformed journalism?
   • Answer: Computer Assisted Reporting (CAR) uses technology to analyse data, helping journalists uncover hidden stories and investigate complex issues like fraud and corruption. By examining large datasets, journalists can identify patterns, trends, and relationships that would be impossible to see manually.
4. Question: What is data visualisation, and why is it important in data journalism?
   • Answer: Data visualisation involves displaying gathered data in graphs, charts, and maps. It’s important because it allows journalists to present complex datasets in an easy-to-understand way, making it accessible to a wider audience and enhancing the impact of their stories.
5. Question: How does data journalism contribute to a more objective and evidence-based approach to reporting?
   • Answer: By including reliable and rich data in stories, data journalism provides a more objective and evidence-based approach to reporting. It allows journalists to back up their reporting with facts and figures, rather than relying solely on opinions.
6. Question: How has the role of a journalist evolved with the rise of data journalism?
   • Answer: The role of a journalist has evolved from simply chronicling events to also becoming an investigator who uncovers patterns and relationships within data. They now use tools to analyse large datasets, test hypotheses, and verify claims, providing deeper insights and accountability.
7. Question: What are some examples of tools used in data journalism?
   • Answer: Tools used in data journalism include spreadsheets, statistical software, and data visualisation platforms. These tools help journalists process and analyse large datasets, making the information more useful for news production.
8. Question: How does data journalism enhance storytelling?
   • Answer: Data journalism enhances storytelling by providing context, nuance, and evidence that deepens public understanding. Visualisations such as maps and charts help translate complex data into clear and impactful narratives.
9. Question: How has collaboration changed in modern data journalism, and why is it important?
   • Answer: Modern data journalism involves increased collaboration among journalists, data scientists, designers, and programmers, often across borders. This collaboration is crucial for tackling complex investigations and sharing datasets, strengthening trust through transparency.
10. Question: What is the significance of data transparency in data journalism?
    • Answer: Data transparency, such as publishing methodologies and sharing datasets, strengthens trust in journalism, especially in times of skepticism. It allows the audience to see where the information came from and verify the findings, promoting accountability and credibility.

Related articles

Data journalism – resources and tools

Data journalism glossary

Good journalism has always been about data

The post What is data journalism? first appeared on Media Helping Media.

It addresses the critical issue of false equivalence and false balance, emphasising the importance of evaluating scientific consensus while also considering alternative perspectives.

Through a combination of theoretical understanding, practical exercises, and case studies, participants will learn to navigate the complexities of climate reporting while maintaining journalistic integrity and avoiding the pitfalls of misinformation.

The lesson is based on four articles on Media Helping Media which we recommend journalism trainers read before adapting this lesson plan to meet local needs. The articles are:

• Covering climate change
• Human contribution to climate change
• Climate change – tone and language
• Climate change – a glossary

Session 1: The science of climate change (2 hours)

• Objectives:
  • Introduce the main scientific concepts of climate change.
  • Set out the scientific consensus on anthropogenic global warming along with alternative explanations.
  • Familiarise participants with key climate data and sources.
• Content:
  • Introduction to the greenhouse effect and its scientifically proven amplification by human activities.
  • Analysis of key scientific studies and reports (IPCC, NASA, NOAA).
  • Examination of global temperature trends, atmospheric CO2 levels, and sea-level rise data.
  • Discussion on the role of climate models and their reliability.
• Activities:
  • Reviewing and discussing scientific papers and data visualisations.
  • Group discussion on the importance of scientific literacy in journalism.
  • Providing links to the resources (above).
• Materials:
  • Scientific reports and data visualisations.
  • Links to relevant websites (NASA, NOAA, IPCC).

Session 2: Dangers of false equivalence and false balance (2 hours)

• Objectives:
  • Define and illustrate false equivalence and false balance.
  • Analyse case studies of misleading climate change reporting.
  • Consider the alternative explanations.
  • Develop strategies for avoiding the pitfalls of trying too hard to achieve ‘balance’.

• Content:
  • Theoretical framework of false equivalence and false balance.
  • Analysis of media examples that demonstrate these issues.
  • Discussion on the ethical implications of misrepresenting scientific consensus.
  • Discussion of the correct proportion of dissenting voices.
• Activities:
  • Case study analysis of news articles and broadcasts.
  • Group exercises on identifying and correcting false balance.
  • Role-playing scenarios of interview situations.
• Materials:
  • Examples of media coverage with false equivalence.
  • Guidelines on ethical reporting.

Session 3: Climate change terminology and language (2 hours)

• Objectives:
  • Familiarise participants with essential climate change terminology.
  • Discuss the importance of using accurate and nuanced language.
  • Practice translating complex scientific terms into accessible language.
• Content:
  • Review of the provided climate change glossary.
  • Discussion on the impact of language on public perception.
  • Strategies for avoiding alarmism and complacency.
  • Review of the importance of avoiding adverbs and adjectives.
• Activities:
  • Glossary quizzes and exercises.
  • Writing exercises on simplifying complex terms.
  • Group discussions on the emotional impact of language.
• Materials:
  • Climate change glossary.
  • Examples of effective climate change communication.

Session 4: Data journalism and multimedia storytelling (2 hours)

• Objectives:
  • Introduce data journalism techniques for climate reporting.
  • Explore multimedia storytelling formats (videos, infographics, etc.).
  • Practice creating data-driven and visually engaging content.
• Content:
  • Introduction to data sources and analysis tools.
  • Techniques for visualising climate data.
  • Best practices for creating multimedia climate stories.
  • Instruction on how to effectively use provided resources.
• Activities:
  • Data analysis exercises using climate datasets.
  • Creation of sample infographics and video storyboards.
  • Presentations of multimedia project ideas.
• Materials:
  • Climate datasets and analysis tools.
  • Examples of effective multimedia climate stories.

Session 5: Ethical journalism and fact-checking (2 hours)

• Objectives:
  • Reinforce ethical journalism principles in climate reporting.
  • Develop fact-checking skills for identifying misinformation and disinformation.
  • Discuss strategies for dealing with unconscious and intended bias.
• Content:
  • Review of ethical guidelines and best practices.
  • Techniques for fact-checking climate claims.
  • Discussion on recognising and mitigating unconscious bias.
  • Discussion on how to deal with disinformation.
• Activities:
  • Fact-checking exercises using real-world examples.
  • Group discussions on ethical dilemmas in climate reporting.
  • Developing strategies for identifying and countering disinformation.
• Materials:
  • Fact-checking resources and tools.
  • Case studies of ethical breaches in climate reporting.

Session 6: Reporting on climate solutions and impacts (2 hours)

• Objectives:
  • Explore strategies for reporting on climate solutions and resilience.
  • Discuss the importance of humanizing climate change stories.
  • Develop skills for interviewing climate experts and affected communities.
• Content:
  • Strategies for highlighting climate solutions and innovations.
  • Techniques for telling impactful human stories.
  • Guidelines for conducting ethical and respectful interviews.
  • Review of the skills needed by climate journalists.
• Activities:
  • Interview practice with simulated climate experts and community members.
  • Group discussions on framing climate change as a collective challenge.
  • Developing project proposals for climate change stories.
• Materials:
  • Interview guidelines and sample questions.
  • Examples of effective climate solution stories.
  • The guides provided in the text.

Summary

The lesson plan covers essential aspects of climate change reporting, including understanding scientific consensus, interpreting climate data, using appropriate language, and avoiding false balance. It also emphasises the importance of ethical journalism, fact-checking, and recognising bias. Participants will learn to communicate complex information effectively, highlight solutions, and frame climate change as a collective challenge. The plan includes practical exercises, such as analysing news articles, conducting interviews, and creating multimedia content, to reinforce learning and develop practical skills.

Related articles

Human contribution to climate change

Covering climate change

Climate change – tone and language

Climate change glossary

The post Lesson: Climate Change first appeared on Media Helping Media.