Ever have the feeling that news used to be more objective? That news organizations — now media empires — have moved into the realm of entertainment? Or that a cluster of news “across the aisle” from your beliefs is completely outrageous?
Many have these feelings, and coverage is rampant on bias and even straight up “fake” facts in news reporting.
With this in mind, we wanted to see if these hunches are valid. Has news gotten more negative over time? Is it a portion of the political spectrum driving this change? Or is it simply that bad things happen in the world and later get reported on?
To jump into this inquiry we utilized Diffbot’s Knowledge Graph. Diffbot is one of the few North American organizations to crawl the entire web. We apply AI-enabled web scrapers to pages that are publicly available to extract entities — think people, places, or things — and facts — think job titles, topics, and funding rounds.
We started our inquiry with some external coverage on bias in journalism provided by AllSides Media Bias Ratings.
With only 2 weeks left till May 4th (be with you), the internet is bursting with excitement over all the work that needs to be done before Yahoo Answers finally 404s.
From scheduling a 2nd COVID vaccine to your annual panic attack at missing the tax filing deadline (you probably didn’t, it was extended to May 17 in the U.S.), there is nothing short of a lengthy agenda for everyone ahead of the shutdown of this iconic website.
Phrases like “the web is held together by [insert ad hoc, totally precarious binding agent]” have been around for a while for a reason.
While the services we rely on tend to sport hugely impressive availability considering, that still doesn’t negate the fact that the macro web is a tangled mess of semi or unstructured data, and site-by-site nuances.
Put this together with the fact that the web is by far our largest source of valuable external data, and you have a task as high reward as it is error prone. That task is web scraping.
As one of three western entities to crawl and structure a vast majority of the web, we’ve learned a thing or two about where web crawling can wrong. And incorporated many solutions into our rule-less Automatic Extraction APIs and Crawlbot.
In this guide we round up some of the most common challenges for teams or individuals trying to harvest data from the public web. And we provide a workaround for each. Want to see what rule-less extraction looks like for your site of interest? Check out our extraction test drive!
A few weeks ago, we ran reviews for a Michelin-reviewed restaurant through our Natural Language API. It was able to tell us what people liked or disliked about the restaurant, and even rank dishes by sentiment. In our analysis, we also noticed something curious. When our NL API pulled out the entity “Covid-19,” it wasn’t always paired with a negative sentiment.
When we mined back in to where these positive mentions of Covid-19 occurred in the reviews, we saw that our NL API appeared to be picking up on language in which restaurant reviewers felt a restaurant had handled Covid-19 well. In other words, when Covid-19 was determined to be part of a positive statement, it was because guests felt relatively safe. Or that the restaurant had come up with novel solutions for dealing with Covid-19.
With this in mind, we set to starting up another, larger analysis.
Hindsight is 20/20. And as we usher in a new president in what has been one of the most tumultuous years in American history, we can begin to see clarity about the forces that moved throughout our jobs, our lives, and our collective imagination.
Another way to put this is that over time we tend to have more context.
Within Diffbot’s Knowledge Graph, one unique lens through which we can leverage the context of semantic data is by looking at the speakers of quotes.
When our AI reads articles it pulls out quotes, and when it can it attributes a speaker to these quotes. As our crawlers traverse the entirety of the public web, sources of quotes are validated and over time some quotes circulate more than others.
When performing a facet search, this lets us essentially show something like a retweet count for the entire web. This answers questions like whose voices are being heard? And what speakers are the most widely cited in a given topic?
To commemorate the end of an era, let’s take a look at a few of the most circulated statements of the last 365 days.
What were the 10 most circulated quotes across the web by President Joe Biden in the last 365 days?
Enough individuals have heard the siren song of Robotic Process Automation to build several $1B companies. Even if you don’t know the “household names” in the space, something about the buzzword abbreviated as “RPA” leaves the impression that you need it. That it boosts productivity. That it enables “smart” processes.
RPA saves millions of work hours, for sure. But how solid is the foundation for processes built using RPA tech?
First off, RPA operates by literally moving pixels across the screen. Repetitive tasks are automated by saving “steps” with which someone would manipulate applications with their mouse, and then enacting these steps without human oversight. There are plenty of examples for situations in which this is handy. You need to move entries from a spreadsheet to a CRM. You need to move entries from a CRM to a CDP. You need to cut and paste thousands or millions of times between two windows in a browser.
These are legitimate issues within back end business workflows. And RPA remedies these issues. But what happens when your software is updated? Or you need to connect two new programs? Or your ecosystem of tools changes completely? Or you just want to use your data differently?
This shows the hint of the first issue with the foundation on which RPA is built. RPA can’t operate in environments in which it hasn’t seen (and received extensive documentation about).
The public web is chock full of indicators with implications for stock prices, commodities prices, supply chain issues, or the general perceived value of an entity. But how do you reliably get these market indicators?
You can search online… and slog through the most popular pages that all your competitors have also looked at. Or you can read a commentator’s take. And likely stay one step removed from the actual information you should be dealing in.
Or you could deal directly with all of the articles on the web. Each annotated with helpful fields you can filter through like sentiment scores, AI-generated topic tags, what country the article was published in, and many others. That’s where Diffbot’s Knowledge Graph (KG) comes in.
The news index of Diffbot’s KG is 50x the size of Google News’ index. And each article entity in the KG is populated with a rich set of fields you can use to actually search the entire web (not just the portion of the web who paid to get in front of you).
In this guide we’ll work through how to set up a global news monitoring query in the KG. And then schedule this query to repeat and email you when new articles surface.
The story: sentiment of news mentions of Gaza fluctuate by as much as 2000% a week. 90% of news mentions about Minneapolis have had negative sentiment through the first week in June 2020 (they’re typically about 50% negative). Positive sentiment news mentions about New York City have steadily increased week by week through the pandemic.
Locations are important. They help form our identities. They bring us together or apart. Governance organizations, journalists, and scholars routinely need to track how one location perceives another. From threat detection to product launches, news monitoring in Diffbot’s Knowledge Graph makes it easy to take a truly global news feed and dissect how entities being talked about.
In this story by DQL discover ways to query millions of articles that feature location data (towns, cities, regions, nations).
How we got there: One of the most valuable aspects of Diffbot’s Knowledge Graph is the ability to utilize the relationships between different entity types. You can look for news mentions (article entities) related to people, products, brands, and more. You can look for what skills (skill or people entities) are held by which companies. You can look for discussions on specific products.
We will get justice. We will get it. We will not let this door close.
– Philonise Floyd, Brother of George Floyd
News coverage this week centered on George Floyd, police, and Donald Trump. COVID-19 related news continue to dominate globally.
That’s the macro story from all Knowledge Graph article published in the last week. But Knowledge Graph article entities provide users with many ways to traverse and dissect breaking news. By facet searching for the most common phrases in articles tagged “George Floyd” you see a nuanced view of the voices being heard.
In this story hopefully you can begin to see the power of global news mentions that can be sliced and diced on so many levels. Wondering how to gain these insights for yourself? Below we’ll work through how to perform these queries in detail.
How we got there: Diffbot’s Knowledge Graph holds hundreds of millions of article entities at any given moment. These articles are of truly global origins, and are parsed by our cutting-edge machine vision and natural language processing systems to take unstructured article data and transform it into structured, query-able entities.
Harnessing the public web as data is one of the smartest things product, marketing, PR, and machine learning teams can do. It also opens up a host of questions.
- What type of data is valuable to us?
- How accurate do we need our data to be?
- How timely do we need our data to be?
- What are our data sources?