How many languages are on Twitter?

According to our most recent experiment, the answer is:  67 .

These are the top languages

languagesTwitter

This experiment was based on a random sample of 22.632.977 Tweets collected using Twitter Streaming API, 23.4.-7.5.2015.

We used the language attribute (“lang”) which comes as a data field of a Tweet to determine the language.

This experiment is still ongoing, the official results will be published in a research paper.

From founder to advisor and what advisory is good for…

Thomas Peruzzi, a successful founder, board member, business angel and investor chose an interesting topic for his talk in the i2c Public Lecture Series: advisory boards. Often seen on startups’ websites, I was aware that many young companies have a team of advisors, however I did not know much concrete about their role, how they would collaborate with startups and when and how a startup would be able find and hire a team of advisors. By focusing his talk on this topic, Tom Peruzzi clarified many of these questions and gave us insights into the perspective of the advisor as well.

Advisory boards help startups to take right strategic decisions

In advisory board typically consists of a team of 3-5 people who use their individual expertise to help a companies’ management team to take the right strategic decisions. By asking the right questions they challenge the management’s assumptions and provide advice ideally leading to improved success. When a startup placed the first product on the market, got some first traction and has the first investor on board, it is the right time to set up a team of advisors. It is of great importance that advisors provide different types of expertise in disciplines which complement the fields of the management and are critical for business development. There are different remuneration schemes, one popular option is to pay advisors with 0.5-3% of company shares, In return advisors would meet with the management team on a regular basis every 1-2 month to discuss business development and assist with critical questions on demand. The relation between the company and the advisors is coined by respect and mutual trust, which makes Non-Disclosure Agreements superfluous as trust and discretion are basic preconditions. An advisor is different from a consultant, in a way that there is a more independent relationship, an advisor does not work for you, but is a long-term companion to the company who provides critical advice as needed.

The importance of advisors and finding the right ones.

As a potential future startup founder I learned from this talk that it is important to keep in mind already in the beginning that the startup you are founding is not going to stay a small, flexible team of friends for a long time, but in the best case is growing fast. Which creates the need for organization on the one hand and the responsibility to take the right decisions is increasing. As a manager you are expected to stick to your decisions and to not change decisions arbitrarily, at the same time you have to deal with a lot of uncertainty. Every professional advice you can get that helps to take the right, or at least prevent you from taking bad decisions is of critical importance. This assumes however that you picked the right people to get advice from, which might not be so easy. Advisors should not only fit in terms of expertise and knowledge to the company but also with their type of personality and communication. My conclusion from this is that you should see any business conference, talk or meeting also as an opportunity to spot potential advisors, maintain a list of professional, potential advisors already early one (maybe even before you found a company), which might help you to find the right advisors, once you need them.

Who dares wins.

Thomas Peruzzi left his secure job in a large IT company to found his own business in a time when his family was expecting the first child and was building a house. While many people would assume this time to be an inappropriate moment to start a new business, Mr. Peruzzi saw in this moment an opportunity to change his path entirely before settling down. While doubling his income in each of the following 8 years he never regretted his decision. For me this was a very impressive moment of this talk and encourages me to question our assumptions on what we consider appropriate.

Talk of Thomas Peruzzi, on 11 March 2015, Public Lecture Series on Innovation by i2C Innovation Center @ Vienna University of Technology

 

How to turn your PhD thesis into a 3-minute StartUp Pitch

Last week I attended an intense 4-day course on how to evaluate the business potential of your research and turn it into a startup.
It was an amazing time with lots of thinking, discussing and pitching. The i2c Innovation Center of the Vienna University of Technology provided us with 7 top experts every day who helped us to develop our business plan and to put this plan into a compelling 3-minute pitch.
With 12 to 16 hour days is was a lot of work but also a lot of fun.

In the end we got the opportunity to pitch our idea in 3 minutes to a jury of investors and experts and with my project StrikeSensor I won the High Potential R&D idea Award! Yeha!

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Source: I2C Facebook Page

My notes and lessons learned from the Vienna University of Technology “i2c StartAcademy”

General

  • Keep it simple. If you cannot explain it in a simple way you haven’t thought about it long enough. Sit down and try to get it clear in your mind.

Start-up – Business Plan Development

  • The lean start up” philosophy is the most promising approach up to date to start a business. It means you should check your hypotheses about customers needs as early as possible, find a lead customer, evaluate the scope of the problem in reality and test and co-develop your solution iteratively.
  •  Startups are not smaller versions of large companies, one of their main target is to search effectively for problems, solutions, people and customers.
  • Most startups fail because their first contact with the customer is too late
  • Startups don’t actually need a CFO, CIO, CTO …. but they do neet a customer development team
  • Try to get a customer sign a constract before the product is available
  • Find out why your customers would love to do business with you, thats your value proposition.
  • Find out how you can generate excitement, thats the value architecture.
  • Find  out how you can earn money, thats your revenue model.
  • Do not confuse product features with customer value. Find out what you get done for your customer. Find out the pains and the gains of your customer.
  • The Business Model Canvas from Osterwalder is a good tool to structure your business model

BusinessModelCanvas

Business Model Canvas for my project StrikeSensor

Pitching

  • The purpose of a pitch is not only to sell a solution – but you sell your product, your technology, your passion and your team.
  • A good structure for your pitch is (1) One-Liner e.g. “StrikeSensor detects labor strikes around the world in real-time” (2) Problem (3) Solution (4) Future Steps (5) Request
  • Then follows the Question & Answer session, anticipate the questions prepare back-up slides!
  • Important things to mention in your pitch: Whats the target customer and the size of the market? Whats special about the team, competences? What makes you different from other solutions? How can you access the target market, what is your network?
  • If you don’t know the answer to a question refer to someone else in your team who knows it.
  • Before the pitch: (1) Check the microphone (2) Check the clicker (3) Make eye-contact with the person moderating to get a sign to start
  • Make the target clear to yourself, visualize the target in your mind, what do you want to achieve? you need to be 100% clear about that yourself.
  • Make sure every part of your pitch, every slides is directed towards that goal.
  • Show facts and metrics! State the business case, how much value/money can be gained/saved by your solution?
  • After each pitch, reflect: what worked what didn’t work! Make it better next time!
  • On the last slide: provide your logo, your company name, your name, your contacts!

Pricing & finances

  • Main costs: R&D, Labor, Marketing, Production, Sales + Personal Expenses of founders (don’t forget that you need to live from it as well!), customer acquisition, Travelling
  • Income: Price * customer
  • Remember that there might be a difference in time, when costs occur and when you get the revenues
  • Investors can raise the equity (in return for shares) or liabilities, low equity but high reliabilities can make it harder to get money from the bank in the future
  • Good tools for Financial Planning: Plan4You provided by WKO, or BACA Business planer
  • Don’t state numbers too detailed that seems unrealistic and unprofessional
  • the goal of the business plan is to provide a rough picture where the journey is going, and how much money you need to request from investors.
  • Sources of funding in Austria
    • AWS: PreSeed: has to adress Venture Capitalists as investors, you should have an exit strategy
    • FFG: funds 70% of costs as liability, 30% is covered by yourself, focus on applied science
    • Others: Business Angels, Family Offices, Venture Capitalists, Banks, Crowd, Founders, Family

Marketing

  • Start with active marketing & sales activities early!
  • Useful Tools:
    • hootsuite for cross-platform posting, helps you to manage social media audiences
    • TweetDeck Monitor multiple keywords or timelines on Twitter
    • Sproutsocial Social Media Management
    • If this then that – IFTTT lets you create recipes (stored procedures) that automatically perform tasks for you, e.g. save your email attachments into dropbox, get an email when a profile pic changes..
    • getsatisfaction.com – Social Media marketing software
    • pr.co – A tool to format Press Releases
  • Provide a press-kit on your website: logo, one-liner, short description of company (3-5 sentences)
  • Read about Growth Hacking (marketing strategy for start-up, grow as fast as possible with low resources)

These were the most important lessons I took from last week, and at this point I want to thank all the mentors and i2c Innovation Center for providing us with that knowledge and the unique opportunity to take part in great programs like this one.

From March on I will attend a 3-semester course on Innovation, I am looking forward!

Sensing Labour Strikes in Indonesian Factories on Twitter

As part of my PhD project, I am currently working on a study to analyze how people tweet about labour strikes in factories. I decided to focus my study on Insonesian factories, as Indonesia shows a high emergence of factories as well as social media use.

Many international brands are supplied by factories in Indonesia, consumers often do not know much about the conditions under which products are produced. In a recent study on factories in Indonesia we found that many supplier factories in Indonesia are represented on Foursquare. This indicates that the manufacturing industry is already reflected – to some extent – on social media.

In a next step, I want to analyze, whether problems with working conditions are apparent in online data. However, first of all, it is hard to define what a “problem” actually is. I decided to particularly look at labor strike events, because these are events were workers themselves stand-up in order to raise public awareness for circumstances they find problematic in form of a protest.

This diagram shows the increase of Tweets during a labour strike in an Adidas Factory on the island Batam mentioning the brand or the island.
Example Strike on Twitter
I repeatedly observed peaks in the amount of Tweets mentioning a factory name or city at the time of the event of a labour-strike.

Some of the questions I want to address next are:

  • Who are the people tweeting about strikes? (media, workers, workers unions?)
  • What are the topics discussed before | during | after a strike?
  • Are there certain phases that can be observed across several strike events?
  • Which language(s) are used? (dialects, formal, informal)

In the middle of November I get the chance to travel to Indonesia to the International Conference on Data and Software Engineering 2014 in Bandung and I will stay about four weeks in Indonesia. I am looking forward to listen to the opinions of Indonesian researchers.

Are Twitter predictions a result of researchers expectations?

In the last years, several researchers showed that Twitter data can be used to predict real-world events, like earthquakes [1], the development of stock-market indicators [2], the outcome of political elections [3], the spread of diseases  [4] or movie box-office sales [5]. Indeed studies provide some promising results that Twitter data can be successfully used for predictions, however, recently several researchers questioned both the predictive power of twitter and applied research methods [6, 7].

It seems there are several challenges which make it hard to verify whether and how well proposed methods actually work:

  • It is expensive to obtain historic Twitter data therefore experiments can not be repeated under same conditions
  • A multitude of decisions have to be taken during data collection (Which API is used?, Which keywords or filtering criteria are used? Which time period is captured?) often these decisions are not sufficiently documented which make it hard to repeat experiments and to apply the method in different settings
  • Many of proposed methods require a predefined list of keywords to filter tweets (e.g. “flu”, “cough”, “H1N1″ … if you want to track a disease) however it’s not quite clear how to compile these lists, so methods rely on the ability of the researcher to define such lists and it is difficult to apply methods in a different context, e.g. countries with a different language.

Given this multitude of decisions and predefined knowledge that is required to conduct the experiments combined with the difficulty to repeat experiments for other researchers, it seems in Twitter prediction research could be at risk to be influenced by the observer-expectancy effect, which means that the researcher subconciously effects the research result.

Or as David Hand wrote, in other words:

“It is quite possible that the most interesting patterns we discover during a data mining exercise will have resulted from measurement inaccuracies, distorted samples or some other unsuspected difference between the reality of the data and our perception of it.” [8]

My colleague Amal Almansour from Kings College in London and I, we were particularly interested into the decisions made during Twitter Prediction research, and we just finished a literature survey and cricially analyzed 24 existing Twitter Prediction studies. In this study, we identified the different actors involved in the typical Twitter research process and their potential impact on the prediction method and respectively the prediction result.

This study is currently in the peer-review process, results will be stated here soon.

Analysing Supplier Locations: A Case Study Based on Indonesian Factories – iKnow 2014

In September, I presented our latest study at the International Conference on Knowledge Technologies and Data-Driven Business (iKnow 2014) in Graz.

In this study, we explored how social and semantic data can be used to monitor risks around supplier factories. We focused our study on Indonesia, as it exhibits both an important position as an outsourcing country for several major brands as well as a high social media usage.

Data sample

We compiled a sample of 139 factories in Indonesia supplying 4 popular companies in the textile, sports and electronics industry. Each factory is described by its name and its address. All data was retrieved from the respective company website.

Main research question

  1. Can user-generated data help to determine the physical location (GPS-coordinates) of supplier factories?
  2. How could we link semantic data to attain risk information about supplier factories?

The most interesting facts and results

1. Mapping Services could map only few factory addresses

Using Google Maps, Nokia Here Maps, Bing Maps, Open Street Maps (Nominatim) to transform the address information into GPS-coordinates we could only retrieve accurate GPS-coordinates for few (20/139) factories. There were considerable differences in the number of addresses which could be transformed to GPS coordinates, and precision levels.

geocoding

2.Most of the factories in our sample have a Foursquare profile

For most of the factories (122/139) we could find a profile on the geo-social network “Foursquare”. Foursquare profiles are created by users, those might in this case be workers or people living around the production site.
Typically users register a location with its name and purpose using mobile devices. Thereby maps are created collectively.

Example4Square

3.Most of the factories were tagged on Wikimapia

Most of the factories (94/139) were tagged by users on the crowdsourced map “Wikimapia”. On Wikimapia users can tag buildings with their names or purpose on satellite pictures, thereby they create maps.

Example Factory Tagged on Wikimapia

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