The topic of a personalised Newspaper is something I’ve been thinking about for a while now (in the back of my mind for a year or so).
The idea that highly targeted content can continuously be delivered to me in a centralized location where I can consume, interact with, discuss with friends and strangers, and share it… something so fantastic that I completely forget what an RSS reader is and can’t remember how I went through life without my newspapers/magazines being dynamic to fit my interests and social graphs.
It’s a concept that has been in the minds of many an entrepreneur but, due to the difficultly of it, there have been no real successes so far. These are a few of my thoughts around how I would define a personalised Newspaper, my experiments with automated content filtering, and some of the functionality and experiences I think a true personalised Newspaper would require.
Definition of a personalised Newspaper
A service that passively delivers and recommends prioritised breaking and timeless content to the user based on their ever changing interests and social graph.
My Experiments with Automated Content Filtering
I have experimented with automated content filtering two times so far. The goal was to take a large amount of content and then automatically filter it down to create a better experience.
ItsTrending (April 2010)
The first experiment was called ItsTrending, a site that kept track of and displayed what content was being shared and liked the most on Facebook. I launched this immediately after Facebook released the Open Graph APIs thinking that it would be the next Digg — but automated — AKA a million times better. Although it attracted users, press and did actually present some interesting automated results — it wasn’t personalised. Just because a spanish music video has the most shares on YouTube right now does not mean that I am interested in it at all.
Conclusion: What is globally popular is not an accurate representation of what everyone likes. personalisation is important.
FriendShuffle (October 2010)
The second experiment was FriendShuffle, a site where you logged in with your Twitter and/or Facebook account and it let you view a slideshow of content your friends were sharing. The thought here was that your friends would be good curators and the result would be a great personalised experience. Turns out you don’t like your friends because of their interests — you like your friends because of shared experiences (college, work, family, etc.). So right away Facebook’s social graph was producing low quality results (except for when they shared YouTube videos… those were always solid). On the other hand, content from Twitter produced much better results — we attributed this to the fact that on Twitter you do actually follow people because you share the same interests.
Conclusion: Personalizing to your social graph does not work. Filtering content based on topic/interest experts/curators does.
Functionality and Experience Requirements
- Serendipity: The ability to discover / be presented with content we don’t regularly consume. This is important for evolving your current interests.
- Editorial: Curators that can filter/prioritise the content in a meaningful way.
- Social: People want to discuss and share topics with friends. It is also important to note that people often hear about breaking news from a friend.
- personalisation: Present topics and content that interests the user. Collect this information (and continue to modify) with no direct input from the user (do it in the background).
- User Interface: It is important to allow simple navigation between the list of topics, a specific topic, a specific publisher, a specific author, and a specific curator.
- Sharing/Broadcasting: It is very important to allow/encourage the user to share or re-share content and/or commentary. This is important for the virality of the product and the value that is added to the content publishers (distribution).
- Content Structure: Topics are important buckets to present to the user.
Concepts That Need To Be Understood
- Breaking News vs. personalised News: personalised News needs to match the user’s personal interests and filters while Breaking News typically applies to a much larger audience.
- Real Time vs. Digest: News can be consumed in real time or as a digest with content/data from a set amount of time.
- News Shelf Life: Some stories are relevant tomorrow while some are only relevant now. The newspaper must understand this about the stories and present them accordingly.
- personalisation and Editorial are entirely different but absolutely required. What you want plus what you may be interested in.
- Recommendations from friends are very powerful but these content recommendations must be dispersed throughout the topics the user is subscribed to (as opposed to showing all friend recommendations for all content in one section).
- Content delivered in the personalised Newspaper must provide the user with a vastly improved experience. The user must commit to consuming content exclusively through the product.
Ultimately the personalised Newspaper must instantly deliver a meaningfully personalised experience upon sign up and continue to evolve with the user’s interests with no manual input required from the user — behavioural/interest data must be passively collected from multiple sources not just limited to user interactions with the newspaper. The consumption and discovery process includes data from friends, trusted topic curators, the general reaction of the entire audience, and most importantly a small amount of content that falls outside of these filters — allowing the user to discover new content and topics they may not have known about.
Send me a message at @MattPRD if you want to discuss!
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