Category Archives: Social networks

TBH is acquired by Facebook

TBH (short for “to be honest”) is an anonymous messaging app that promotes positive communications rather than the negative communications that often plague anonymous messaging apps. We had invested in the app’s parent company at Aslanoba Capital, and TBH was recently acquired by Facebook.

At the time of our investment in February 2015, TBH’s parent company was working on an on-campus messaging app, not the anonymous messaging app that is currently TBH. The team then went through several iterations of other messaging apps before finding success with TBH. This article outlines their journey well. In other words, TBH wasn’t an overnight success, but the result of perseverance, learning, and continual growth.

I commend the TBH team and its CEO Nikita Bier for each of these three traits.

Understanding social platforms

In a post around this time last year, I shared VersionOne’s “A Guide to Marketplaces” as an excellent primer on the different types of marketplace companies and their underlying characteristics.

This year, VersionOne published a similar report on Understanding Social Platforms. The report gives a great overview of the different types of social platforms including messaging, private social networks, public social networks, enterprise social platforms, and communities. It also highlights the characteristics which are common across and unique to each platform, and offers ways to think about and measure the performance of different platforms.

You can read the full report here.

News algorithms with a human touch

3 weeks ago, I wrote about how the explosion in news content and the challenge of fact-checking this news content is leading to widespread misinformation.

The timing of the post was particularly appropriate as, following the results of the US presidential elections, reports have emerged that draw attention to how people are producing articles with false information designed to attract clicks and generate revenue, and spreading these articles on social media sites like Facebook. The argument is that these factually incorrect articles helped Trump win the US election.

I don’t agree with this assessment. These articles containing false information are just one of several factors which contributed to the election outcome. And they’re a small one at that. In addition, although perhaps to different extents, they impacted both candidates.

However, the widespread distribution of these factually incorrect articles does lead to an important question. What responsibility, if any, do social media sites like Facebook have to monitor the factual accuracy of the content that they’re helping spread?

Facebook argues that it doesn’t have this responsibility because it is simply a distributor of content. It is not a media company that produces the content.

While true, distributors of online news content have a very different role than offline distributors of newspapers. Online content is effectively infinite while offline content isn’t. This gives online content distributors the ability to influence what readers consume to a far greater extent than offline newsstands. While a newsstand could display all the newspapers available in the country, Facebook has to choose what content to display within your newsfeed. It’s physically impossible to show it all.

So far, Facebook has chosen to prioritize the content it displays based on a black box algorithm which appears designed to maximize user engagement and hence Facebook’s revenue. The problem with this approach is that few users care about the facts. Most are just looking for the next adrenaline rush. So content which meets this demand gets clicks and is pushed to the top of the news feed where it gets more clicks, irrespective of factual accuracy.

But if this isn’t the right approach because factually correct content is intrinsically valuable and this approach often directs our attention to factually incorrect content, then what is the right approach?

One possibility is for Facebook to have a fact checking team, or to work with a third party fact checking team, to only surface content that it deems factually correct. The problem with this approach is that, whenever humans have absolute power like this, it’s up for abuse. One of social media’s greatest advantages over traditional media is that it doesn’t exercise editorial influence (at least in most cases). Allowing Facebook to be the arbiter of factual accuracy would give it much greater editorial powers. This is dangerous and should be avoided.

I believe that the solution lies at the middle of these two extremes. An engagement-optimizing algorithm isn’t the solution, but a human fact-checking team that can override the algorithm whenever it wants to isn’t the solution either.

Instead, Facebook’s algorithm needs to evolve to include factual accuracy as one of the important variables which it uses to determine which articles to surface in its users’ news feeds. This is similar to Google’s search results reflecting not only the number of links to a specific page but also the quality of the sites providing these links. I don’t know the variables taken into account in Facebook’s algorithm but I doubt that factual accuracy is a variable with an important weight, if it is even a variable at all.

I recognize that what I’m proposing isn’t a perfect solution.

The factual accuracy variable will be subject to human bias, at least until we get machines to perform fact-checking for us. But even then, these machines will initially be designed by humans so they’ll also continue to reflect our biases.

And false articles that get clicks may still surface at the top of news feeds if their engagement levels overcome the weight of the penalty they receive due to their factual inaccuracy.

However, a perfect solution doesn’t exist. The best we can hope for is to reward factual accuracy as much as we can without giving the humans responsible for deciding on this factual accuracy limitless power. Enhancing an algorithm with a human touch (not a human override) is the best option available.

This leaves three questions outstanding.

First, what weight will be assigned to the factual accuracy variable?

Second, how and by whom will the variable be measured?

Third, what motivation (or regulation) will ensure that Facebook adopts a factual accuracy variable that lowers its user engagement and revenue?

Transactional services in messaging apps

Facebook Messenger recently announced that it has partnered with Uber to let Messenger users order Uber cars from within the Messenger app. The company is taking a page out of the playbook of WeChat, China’s largest messaging app that lets users engage in transactional services from within its app.

This is likely the first of many transactional services that will be made available on Facebook Messenger. For example, WeChat also lets its users order food, book doctor appointments, check in to flights, and send money to their friends from within its app. In doing so, messenger services help transactional apps gain direct exposure to hundreds of millions of users. In exchange, they have the opportunity to earn revenue for the exposure that they’re providing each app. This effectively transforms them into alternatives for Apple and Android’s app stores.

Most transactional services, like ordering cars and food, are local in nature. Messenger services need to partner with local players in each market to offer their users access to these services. This is why Facebook’s partnership with Uber in the US currently isn’t reflected when a user launches the Messenger app in Turkey. I can’t order a car as a Messenger user in Turkey.

It will be interesting to see whether global messaging apps will establish partnerships with the leading transactional service providers in Turkey before a local messaging app does. Bip is likely the leading candidate for the latter. Their Discover feature, which I’ve shared screenshots of below, suggests that they’re aware of the opportunity.

IMG_1712                 IMG_1713

Anonymous messaging

The recent rise, and in some cases subsequent fall, of anonymous messaging apps like Yik Yak, Whisper, and Secret has brought a lot of media and investor attention to the anonymous messaging space. Each of these companies is an example of 1-to-n anonymous messaging for the consumer. In other words, a user broadcasts an anonymous message to the other users of an app who are then able to interact with the original message by performing actions like up voting, commenting on, and sharing the message.

We already know from messaging apps where users are tied to their real identity that 1-to-n messaging is only one form of communication. The likes of Whatsapp, Facebook Messenger, Line, and WeChat also allow for 1-to-1 and n-to-n communication. I believe that these use cases also exist in the anonymous communication space.

It is with this hypothesis in mind that we recently invested in and Five.

Short for, is a 1-to-1 anonymous messaging app with over 3M users. Founded by Turkish entrepreneur Ozan Yerli, over 50% of’s users are currently from Turkey. These 1.5M users represent over 15% of’s target 10M population between the ages of 13 and 24 in Turkey. The company also has over 200,000 users in the US and has been primarily focused on growing this number since opening its Silicon Valley office in December 2014.

Founded by Nikita Bier, Five is an n-to-n semi-anonymous messaging app that hopes to recreate the chat room experience of the early 2000’s on mobile. The platform is actually a restricted version of n-to-n as it allows for only 5 people per chat room. This is the number that Five has discovered optimizes the quality of a conversation by ensuring that there is a sufficient quantity of communication without too much noise. The platform is also semi-anonymous in that users log in with an avatar of their face rather than in complete anonymity. This allows for more accountability than a completely anonymous setting while respecting the needs of users who don’t want to reveal their real name in the chat rooms.

In addition to our investments in the consumer chat space, I also made a personal investment in anonymous enterprise feedback platform BetterCompany. Founded by Tom Williams, BetterCompany allows workers to share anonymous feedback with their co-workers. Rather than allow for unstructured feedback which risks producing overwhelmingly critical and sometimes even harmful comments, BetterCompany uses a structured approach to ensure that workers congratulate the strengths while also highlighting the weaknesses of their co-workers.

As our investments show, we’re strong believers in both consumer and enterprise applications in the anonymous messaging space. Anonymous communication is a clear need which, with the right product features in place, can be met without sacrificing constructive exchanges.


Bip is the new messaging app of Turkcell, Turkey’s largest mobile operator. I came across ads for the app on several online and offline channels and decided to check it out.

Bip is a cross between WhatsApp and Snapchat with additional emoticons specific to the Turkish market. It allows you to send messages to people on your phone’s contact list who have Bip installed, just like on Whatsapp. But you can also add a timer to your message so that it disappears after a certain time like on Snapchat. Snapchat messages are set to disappear after between 1 and 10 seconds while messages on Bip can be set to disappear after 3, 5, 10, or 60 seconds.

Bip also features local emoticons with Turkish text and images specific to Turkish culture. I liked these.

The app’s data usage is free for Turkcell subscribers but not subscribers of other operators. This gives it an advantage over other messaging apps for Turkcell subscribers.

Bip is a strong attempt by a mobile operator to lure smartphone users away from third party apps to their own communication platforms. It adds a nice local touch to competitors like Whatsapp and Snapchat, and I look forward to following its progress over the coming months.