If you have a mobile phone made in the last eight years, or if you've used social media, you're likely familiar with emoji. The colorful icons, first available in Japan in the 1990s, are ubiquitous and an increasingly common part of our online lives. They have all but replaced emoticons, their punctuation-based precursors, though kaomoji (more detailed emoticons, originating in Japan) such as ᕕ( ᐛ )ᕗ still enjoy popularity in some corners of the internet. Perhaps the most compelling example of emoji popularity was the "face with tears of joy" emoji 😂 being selected as the Oxford Dictionaries Word of the Year in 2015 - a fact you will find in the introduction of many academic papers on the topic.
A week today sees the biggest SAGE Ocean event to date as we takeover the RocketSpace Theatre to bring you an exciting evening of drinks and discussions around diversity and gender equality in academia and in particularly, social data science. Sorry to all those people scattered further afield in the UK who can’t make it to London but fear not, we will be filming the event and the recording should be available later in the year.
If we were to do a text mining exercise on all the incredible discussions at last week’s conference 100+ Brilliant Women in AI & Ethics, education would beat all other topics by a mile. We talked about educating kids, we had teenagers share their thoughts on AI in poems and essays, and exchanged views on the nuances of teaching ethics in computing and working with large volumes of social data both for computer scientists and experts from other disciplines.
The 2nd-4th September 2019 marked the third in a series of symposia on Societal Challenges in Computational Social Science (Euro CSS). Computer scientists, political scientists, sociologists, physicists, mathematicians and psychologists from 24 countries gathered in Zurich for a day of workshops and tutorials followed by a two-day one track conference.
Transcribing is a pain, recent progress in speech recognition software has helped, but it is still a challenge. Furthermore, how can you be sure that your person-identifiable interview data is not going to be listened and transcribed by someone who wasn’t on your consensus forms. The bigger disruptor is the ability to annotate video and audio files
On the 28th of August, we visited sunny Georgetown University to discuss all things politics and Computational Social Science for the second annual PaCSS conference. Here are our highlights.
AI is susceptible to misuse and has been found to reflect biases that exist in society. Fortunately, there are a number of organizations committed to addressing ethical questions in AI. We list our top 10.
In The Code: Silicon Valley and the Remaking of America, Margaret O’Mara provides a new account of the region’s evolution that brings the US government into the story. The book offers a compelling narrative that tracks the key players and events that have underpinned Silicon Valley’s tremendous, but messy, rise, writes Robyn Klingler-Vidra, while also underscoring the gender imbalance and casual misogyny that has been a longstanding characteristic of its culture.
Coming from a social science background, I have had very limited exposure to data science. I was therefore excited to learn about the emerging field of computational social science and the Summer Institute in Computational Social Science (SICSS) presented the right opportunity. I applied to the 2019 SICSS and I was accepted for the Cape Town partner site. I went in not knowing what to expect but by the end of the first day I knew the experience at the two-week Summer Institute was going to be truly worthwhile.
This summer we've had the pleasure of welcoming four Masters students from UK universities to work with the SAGE Ocean team. All four students have been quite incredible, and have managed to produce a variety of outputs and substantially contribute to our work. In this blog post, they share testimonials of their time in the team.
Whether you call it ‘content analysis’, ‘textual data labeling’, ‘hand-coding’, or ‘tagging’, a lot more researchers and data science teams are starting up annotation projects these days. Many want human judgment labeled onto text to train AI (via supervised machine learning approaches). Others have tried automated text analysis and found it wanting. Now they’re looking for ways to label text that aren’t so hard to interpret and explain.
In April this year a special collection examining social media and politics was published in SAGE Open. Guest edited by Joshua A. Tucker and Pablo Barberá, the articles grew out of a series of conferences held by NYU’s Social Media and Political Participation lab (SMaPP) and the NYU Global Institute for Advanced Study (GIAS) known as SMaPP-Global. Upon publication Joshua Tucker said ‘the collection of articles also shows the value of exposing researchers from a variety of disciplines with similar substantive interests to each other's work at regular intervals’. Interdisciplinary collaborative research projects are a cornerstone of what makes computational social science such an interesting field. We were intrigued to know more so caught up with Josh and Pablo to hear more.
The baton was passed to the University of Amsterdam for the fifth addition of IC2S2 with the core conference taking place between 18-20 July.
Text is everywhere, and everything is text. More textual data than ever before are available to computational social scientists—be it in the form of digitized books, communication traces on social media platforms, or digital scientific articles. Researchers in academia and industry increasingly use text data to understand human behavior and to measure patterns in language. Techniques from natural language processing have created a fertile soil to perform these tasks and to make inferences based on text data on a large scale.
Academics face various pressures, from research teaching and administrative duties. The best way to create a positive culture in academia is to share. However, it may sometimes feel like there is no incentive to share teaching materials, if I have spent so many hours developing this work, why should I just hand it over to someone, “what’s in it for me?”
For centuries, being a scientist has meant learning to live with limited data. People only share so much on a survey form. Experiments don’t account for all the conditions of real world situations. Field research and interviews can only be generalized so far. Network analyses don’t tell us everything we want to know about the ties among people. And text/content/document analysis methods allow us to dive deep into a small set of documents, or they give us a shallow understanding of a larger archive. Never both. So far, the truly great scientists have had to apply many of these approaches to help us better see the world through their kaleidoscope of imperfect lenses.
It’s all about incentives. The current academic ecosystem incentivises publication in high impact factor journals and grant capture above all else, but there is more to being an academic than producing journal articles and winning grants. Luckily there are an increasing number of initiatives that are helping academics get credit for more of the work they do and increase their broader impact. This post rounds up some of the most interesting efforts.
Here at SAGE Ocean, we’ve been collecting data on the landscape of tools for computational social science. While looking through the data, we found an incredible variety, from resources to aid crowdsourcing to text analysis to social media analysis. Despite this diversity at the technical level of the tools, we found a persistent lack of diversity in terms of who built these tools.
Today, researchers are using LinkedIn data in a variety of ways: to find and recruit participants for research and experiments (Using Facebook and LinkedIn to Recruit Nurses for an Online Survey), to analyze how the features of this network affect people’s behavior and identity or how data is used for hiring and recruiting purposes, or most often to enrich other data sources with publicly available information from selected LinkedIn profiles (Examining the Career Trajectories of Nonprofit Executive Leaders, The Tech Industry Meets Presidential Politics: Explaining the Democratic Party’s Technological Advantage in Electoral Campaigning).
At CogX, the Festival of AI and Emergent Technology, two icons appeared over and over across the King’s Cross location. The first was the logo for the festival itself, an icon of a brain with lobes made up of wires. The second was for the 2030 UN Sustainable Development Goals (SDGs), a partner of the festival. The SDG icon is a circle split into 17 differently colored segments, each representing one of the goals for 2030—aims like zero hunger and no poverty. The idea behind this partnership was to encourage participants of CogX—speakers, presenters, expo attendees—to think about how their products and innovations could be used to help achieve these SDGs.