The best way to prevent fake news from being consumed by your researchers is to control the quality of the sources included in your news feed. Sources like the ones we include our Northern Light Business News feed will never originate and only rarely republish fake news stories.
Fake news can only go viral when someone makes up a story and then publishes it on a website. Because many web news sites do not originate their own content but use automated means to find stories on other web sites, they end up republishing these stories. That means fake news stories spread across the web at the speed that software algorithms can copy and republish them, which is very fast.
Every one of the news sources included in Northern Light Business News must passes muster with our editorial department. The source must have only original, high quality content that provides analysis, commentary, and perspective. The source must employ journalists who use bylines and who are knowledgeable about the industries they write about. Such sources and journalists are unlikely to fall for a fake news story and they rarely republish other source’s material.
While we can’t claim that our editorial practices guarantee we will never ever include a fake news story in our feed, it would have to be a story that has already convinced knowledgeable and careful journalists. This eliminates a huge part of the risk of further disseminating fake news when using our news feed.
by Anton Voskresenskiy, Chief Research Officer
When Northern Light started its machine learning project we paused to consider the areas where machine learning could add value to search for our clients. We listened to what our clients and users were saying about the nature of search and how they wished it were different.
There is no doubt that cognitive styles are evolving and that users want the machine to do more of the work. These are the things our users told us:
“When I want to drill into a topic more deeply from a search result, why can’t the machine figure out what the right query is for me? I don’t want to think about topics and query terms to add to my search terms.”
“Why can’t the machine just read the documents on the search result for me and tell me what it finds that I should know?”
“Why do I have to search at all? Why can’t the machine just monitor the content flow and tell me when there is something new I should pay attention to?”
So we set out to address these pain points with our machine learning development effort. We think we have made some innovative strides that contribute to a solution of the problems expressed above.
- Recommended Reading List: uses machine learning to observe what an individual user is interested in and then monitors the content flow for new material the user should care about. You don’t search for the content; the content comes and finds you!
- More Like This: uses machine learning to rewrite the query based on an article or report of interest to find additional material that is on point for the user.
- Insights Report: uses machine learning to find important ideas in the documents on the search result page and pulls the sentences that represent those ideas into a report for the user to read.
Each one of the above features is in effect an intelligent assistant to help you find more strategic insights. The next year will be an exciting one for Northern Light and our clients. Machine intelligence is arriving like an eager new employee to start its job working for us – and we will all be learning together how to use it to lever our human intellects. We can’t wait to share it with you!
Recommended Reading List for IT News
More Like This and Insights Report from “More Recommended Articles”
Continuing the theme of books that have implications for meaning extraction, this post comments upon On Being Certain by Robert Burton, M.D. Burton is the associate chief of the Department of Neurosciences at Mt. Zion- University of California Hospital. (His website is rburton.com.) Continue reading On Being Certain
One of the books that inspired my thinking on meaning extraction was On Intelligence by one of Silicon Valley’s most successful computer architects, Jeff Hawkins, and highly-regarded science writer Sandra Blakeslee. Hawkins founded the Redwood Neuroscience Institute to study memory and cognition, but he is not just an academician doing brain research. Rather, he is a substantial practitioner, being the CTO of palmOne and counting the creation of the PalmPilot among his accomplishments. I am always attracted to those persons that not only think a lot about a problem, but who then solve the myriad of practical problems required to translate those thoughts into devices and processes out here in the concrete reality. (A colleague recently described me as a “poster boy for pragmatism.”) Continue reading On Intelligence: Lessons for Meaning Extraction
Some enterprise computing applications, such as manufacturing systems, financial reporting, and email, will always be the responsibility of a centralized IT organization. But some applications are ideal for other IT delivery models. Continue reading Software as a Service to the Rescue: Part 3 – Make the IT Department the Hero, Not the Goat
Over the years, I’ve spoken and written frequently about the challenges of managing and extracting maximum value from licensed market research across an enterprise. It’s a complicated (and potentially risky) endeavor — especially when content from multiple third-party publishers is involved, which is typically the case at global, research-driven organizations. My latest on this topic is posted at AIIM president John Mancini’s “Digital Landfill” blog — http://aiim.typepad.com/aiim_blog/2010/06/index.html. Check it out and let me know what you think.
Corporate IT groups are no strangers to designing and enforcing enterprise data security regimens – but the type of security required for a strategic research portal incorporating licensed content from third-party publishers is a whole different ballgame. It’s one of the reasons a Software as a Service-based portal solution is so appealing to many large organizations. Continue reading Software as a Service to the Rescue – Part 2: Complexities of Document Level Security
Corporate IT groups are very good at a lot of things, but developing and managing systems that deal with external content repositories – especially valuable research content licensed from third-party providers on anything less than an enterprise-wide basis – isn’t one of them. That’s why a SaaS-based “portal” solution for “strategic research” such as market research, competitive intelligence, product development, and technology research makes good sense, resulting in some of the world’s largest research-driven companies choosing to go that route. Continue reading SaaS to the Rescue – Part 1: Interfacing to Third-Party Publishers
Search is the enterprise application that suffers the most from reinvention of the wheel. While you are researching a business opportunity, competitive development, or market event using a search engine, other people in your company are probably repeating the same search. They may be in an office near you, on another floor of the building, across the corporate campus, or in a business unit on another continent. There is no greater loss productivity from any enterprise application than in the search process when many times each day employees in different locations, divisions, and product groups of the same company, unbeknownst to each other, search their enterprise repositories and licensed market intelligence information for the same (or closely related) pieces of information. The search engine industry has even institutionalized this problem, making it a “feature not a bug,” by popularizing notions such as “most popular queries.” Think about it, most popular queries? How again is it good that same query is repeated over and over? Continue reading Why Search Is the Wrong Answer To the Wrong Problem
There are times I think the text analytics industry has painted itself into a corner with sentiment scoring. Not too long ago I attended an industry event in which every provider of text analytics that presented talked about how their solution could do sentiment scoring, and also a few other things. Speaker after speaker, I thought the “few other things” mentioned in passing were way more useful than sentiment scoring. But the speakers appeared to feel that sentiment scoring was what text analytics is about, at least from a PR and marketing perspective. Continue reading Beyond Sentiment Scoring