Our knowledge of how automated agents interact is rather poor (and that could be a problem)

by taha yasseri 14/06/2017

We might expect bot interactions to be relatively predictable and uneventful.

by taha yasseri 14/06/2017

Wikipedia uses editing bots to clean articles: but what happens when their interactions go bad? Image of "Nomade", a sculpture in downtown Des Moines by Jason Mrachina (Flickr CC BY-NC-ND 2.0).

Recent years have seen a huge increase in the number of bots online—including search engine Web crawlers, online customer service chat bots, social media spambots, and content-editing bots in online collaborative communities like Wikipedia. (Bots are important contributors to Wikipedia, completing about 15% of all Wikipedia edits in 2014 overall, and more than 50% in certain language editions.) While the online world has turned into an ecosystem of bots (by which we mean computer scripts that automatically handle repetitive and mundane tasks), our knowledge of how these automated agents interact with each other is rather poor. But being automata without capacity for emotions, meaning-making, creativity, or sociality, we might expect bot interactions to be relatively predictable and uneventful. In their PLOS ONE article “Even good bots fight: The case of Wikipedia”, Milena Tsvetkova, Ruth García-Gavilanes, Luciano Floridi, and Taha Yasseri analyse the interactions between bots that edit articles on Wikipedia. They track the extent to which bots undid each other’s edits over the period 2001–2010, model how pairs of bots interact over time, and identify different types of interaction outcomes. Although Wikipedia bots are intended to support the encyclopaedia—identifying and undoing vandalism, enforcing bans, checking spelling, creating inter-language links, importing content automatically, mining data, identifying copyright violations, greeting newcomers, etc.—the authors find they often undid each other’s edits, with these sterile “fights” sometimes continuing for years. They suggest that even relatively “dumb” bots may give rise to complex interactions, carrying important implications for Artificial Intelligence research. Understanding these bot-bot interactions will be crucial for managing social media, providing adequate cyber-security, and designing autonomous vehicles (that don’t crash). We caught up with Taha Yasseri and Luciano Floridi to discuss the implications of the findings: Ed.: Is there any particular difference between the way individual bots interact (and maybe get bogged down in conflict), and lines of vast and complex code interacting badly, or having unforeseen results (e.g. flash-crashes in automated trading):…

Politics & Government

Can we predict electoral outcomes from Wikipedia traffic?

by taha yasseri 06/12/2016

Data show that the relative change in page views to the general Wikipedia page on the election can offer an estimate of the relative change in election turnout.

by taha yasseri 06/12/2016

2016 presidential candidate Donald Trump in a residential backyard near Jordan Creek Parkway and Cody Drive in West Des Moines, Iowa, with lights and security cameras. Image by Tony Webster (Flickr).

As digital technologies become increasingly integrated into the fabric of social life their ability to generate large amounts of information about the opinions and activities of the population increases. The opportunities in this area are enormous: predictions based on socially generated data are much cheaper than conventional opinion polling, offer the potential to avoid classic biases inherent in asking people to report their opinions and behaviour, and can deliver results much quicker and be updated more rapidly. In their article published in EPJ Data Science, Taha Yasseri and Jonathan Bright develop a theoretically informed prediction of election results from socially generated data combined with an understanding of the social processes through which the data are generated. They can thereby explore the predictive power of socially generated data while enhancing theory about the relationship between socially generated data and real world outcomes. Their particular focus is on the readership statistics of politically relevant Wikipedia articles (such as those of individual political parties) in the time period just before an election. By applying these methods to a variety of different European countries in the context of the 2009 and 2014 European Parliament elections they firstly show that the relative change in number of page views to the general Wikipedia page on the election can offer a reasonable estimate of the relative change in election turnout at the country level. This supports the idea that increases in online information seeking at election time are driven by voters who are considering voting. Second, they show that a theoretically informed model based on previous national results, Wikipedia page views, news media mentions, and basic information about the political party in question can offer a good prediction of the overall vote share of the party in question. Third, they present a model for predicting change in vote share (i.e., voters swinging towards and away from a party), showing that Wikipedia page-view data provide an important increase…

Social Data Science

Edit wars! Examining networks of negative social interaction

by taha yasseri 04/11/2016

While these interactions are less common, they strongly affect people’s psychological well-being, physical health, and work performance.

by taha yasseri 04/11/2016

Network of all reverts done in the English language Wikipedia within one day (January 15, 2010). Read the full article for details

While network science has significantly advanced our understanding of the structure and dynamics of the human social fabric, much of the research has focused on positive relations and interactions such as friendship and collaboration. Considerably less is known about networks of negative social interactions such as distrust, disapproval, and disagreement. While these interactions are less common, they strongly affect people’s psychological well-being, physical health, and work performance. Negative interactions are also rarely explicitly declared and recorded, making them hard for scientists to study. In their new article on the structural and temporal features of negative interactions in the community, Milena Tsvetkova, Ruth García-Gavilanes and Taha Yasseri use complex network methods to analyse patterns in the timing and configuration of reverts of article edits to Wikipedia. In large online collaboration communities like Wikipedia, users sometimes undo or downrate contributions made by other users; most often to maintain and improve the collaborative project. However, it is also possible that these actions are social in nature, with previous research acknowledging that they could also imply negative social interactions. The authors find evidence that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. However, they don’t find evidence that editors “pay forward” a revert, coordinate with others to revert an editor, or revert different editors serially. These interactions can be related to the status of the editors. Even though the individual reverts might not necessarily be negative social interactions, their analysis points to the existence of certain patterns of negative social dynamics within the editorial community. Some of these patterns have not been previously explored and certainly carry implications for Wikipedia’s own knowledge collection practices—and can also be applied to other large-scale collaboration networks to identify the existence of negative social interactions. Read the full article: Milena Tsvetkova, Ruth García-Gavilanes and Taha Yasseri (2016) Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in…

Politics & Government

Brexit, voting, and political turbulence

by taha yasseri 18/08/2016

Explaining why many political mobilisations of our times seem to come from nowhere.

by taha yasseri 18/08/2016

Cross-posted from the Princeton University Press blog. The authors of Political Turbulence discuss how the explosive rise, non-normal distribution and lack of organisation that characterises contemporary politics as a chaotic system, can explain why many political mobilisations of our times seem to come from nowhere.

On 23rd June 2016, a majority of the British public voted in a referendum on whether to leave the European Union. The Leave or so-called #Brexit option was victorious, with a margin of 52% to 48% across the country, although Scotland, Northern Ireland, London and some towns voted to remain. The result was a shock to both leave and remain supporters alike. US readers might note that when the polls closed, the odds on futures markets of Brexit (15%) were longer than those of Trump being elected President. Political scientists are reeling with the sheer volume of politics that has been packed into the month after the result. From the Prime Minister’s morning-after resignation on 24th June the country was mired in political chaos, with almost every political institution challenged and under question in the aftermath of the vote, including both Conservative and Labour parties and the existence of the United Kingdom itself, given Scotland’s resistance to leaving the EU. The eventual formation of a government under a new prime minister, Teresa May, has brought some stability. But she was not elected and her government has a tiny majority of only 12 Members of Parliament. A cartoon by Matt in the Telegraph on July 2nd (which would work for almost any day) showed two students, one of them saying ‘I’m studying politics. The course covers the period from 8am on Thursday to lunchtime on Friday.’ All these events—the campaigns to remain or leave, the post-referendum turmoil, resignations, sackings and appointments—were played out on social media; the speed of change and the unpredictability of events being far too great for conventional media to keep pace. So our book, Political Turbulence: How Social Media Shape Collective Action, can provide a way to think about the past weeks. The book focuses on how social media allow new, ‘tiny acts’ of political participation (liking, tweeting, viewing, following, signing petitions and so on), which turn social movement theory…

Social Data Science

Sexism Typology: Literature Review

by taha yasseri 31/05/2016

Drawing on the rich history of gender studies in the social sciences, coupling it with emerging computational methods for topic modelling, to better understand the content of reports to the Everyday Sexism Project.

by taha yasseri 31/05/2016

The Everyday Sexism Project catalogues instances of sexism experienced by women on a day to day basis. We will be using computational techniques to extract the most commonly occurring sexism-related topics.

As Laura Bates, founder of the Everyday Sexism project, has recently highlighted, “it seems to be increasingly difficult to talk about sexism, equality, and women’s rights” (Everyday Sexism Project, 2015). With many theorists suggesting that we have entered a so-called “post-feminist” era in which gender equality has been achieved (cf. McRobbie, 2008; Modleski, 1991), to complain about sexism not only risks being labelled as “uptight”, “prudish”, or a “militant feminist”, but also exposes those who speak out to sustained, and at times vicious, personal attacks (Everyday Sexism Project, 2015). Despite this, thousands of women are speaking out, through Bates’ project, about their experiences of everyday sexism. Our research seeks to draw on the rich history of gender studies in the social sciences, coupling it with emerging computational methods for topic modelling, to better understand the content of reports to the Everyday Sexism Project and the lived experiences of those who post them. Here, we outline the literature which contextualises our study. Studies on sexism are far from new. Indeed, particularly amongst feminist theorists and sociologists, the analysis (and deconstruction) of “inequality based on sex or gender categorisation” (Harper, 2008) has formed a central tenet of both academic inquiry and a radical politics of female emancipation for several decades (De Beauvoir, 1949; Friedan, 1963; Rubin, 1975; Millett, 1971). Reflecting its feminist origins, historical research on sexism has broadly focused on defining sexist interactions (cf. Glick and Fiske, 1997) and on highlighting the problematic, biologically rooted ‘gender roles’ that form the foundation of inequality between men and women (Millett, 1971; Renzetti and Curran, 1992; Chodorow, 1995). More recent studies, particularly in the field of psychology, have shifted the focus away from whether and how sexism exists, towards an examination of the psychological, personal, and social implications that sexist incidents have for the women who experience them. As such, theorists such as Matteson and Moradi (2005), Swim et al (2001) and Jost and…

Methods, Social Data Science, Tools

P-values are widely used in the social sciences, but often misunderstood: and that’s a problem.

by taha yasseri 07/03/2016

We need to make standards for interpreting p-values more stringent, and also improve transparency in the academic reporting process.

by taha yasseri 07/03/2016

P-values are widely used in the social sciences, especially ‘big data’ studies, to calculate statistical significance. Yet they are widely criticised for being easily hacked, and for not telling us what we want to know. Many have argued that, as a result, research is wrong far more often than we realize. In their recent article P-values: Misunderstood and Misused OII Research Fellow Taha Yasseri and doctoral student Bertie Vidgen argue that we need to make standards for interpreting p-values more stringent, and also improve transparency in the academic reporting process, if we are to maximise the value of statistical analysis. “Significant”: an illustration of selective reporting andstatistical significance from XKCD. Available online athttp://xkcd.com/882/ In an unprecedented move, the American Statistical Association recently released a statement (March 7 2016) warning against how p-values are currently used. This reflects a growing concern in academic circles that whilst a lot of attention is paid to the huge impact of big data and algorithmic decision-making, there is considerably less focus on the crucial role played by statistics in enabling effective analysis of big data sets, and making sense of the complex relationships contained within them. Because much as datafication has created huge social opportunities, it has also brought to the fore many problems and limitations with current statistical practices. In particular, the deluge of data has made it crucial that we can work out whether studies are ‘significant’. In our paper, published three days before the ASA’s statement, we argued that the most commonly used tool in the social sciences for calculating significance—the p-value—is misused, misunderstood and, most importantly, doesn’t tell us what we want to know. The basic problem of ‘significance’ is simple: it is simply unpractical to repeat an experiment an infinite number of times to make sure that what we observe is “universal”. The same applies to our sample size: we are often unable to analyse a “whole population” sample and so have to…

Social Data Science

Topic modelling content from the “Everyday Sexism” project: what’s it all about?

by taha yasseri 03/03/2016

How does the topic modelling algorithm ‘discover’ the topics within the context of everyday sexism?

by taha yasseri 03/03/2016

We recently announced the start of an exciting new research project that will involve the use of topic modelling in understanding the patterns in submitted stories to the Everyday Sexism website. Here, we briefly explain our text analysis approach, “topic modelling”. At its very core, topic modelling is a technique that seeks to automatically discover the topics contained within a group of documents. ‘Documents’ in this context could refer to text items as lengthy as individual books, or as short as sentences within a paragraph. Let’s take the idea of sentences-as-documents as an example: Document 1: I like to eat kippers for breakfast. Document 2: I love all animals, but kittens are the cutest. Document 3: My kitten eats kippers too. Assuming that each sentence contains a mixture of different topics (and that a ‘topic’ can be understood as a collection of words (of any part of speech) that have different probabilities of appearance in passages discussing the topic), how does the topic modelling algorithm ‘discover’ the topics within these sentences? The algorithm is initiated by setting the number of topics that it needs to extract. Of course, it is hard to guess this number without having an insight on the topics, but one can think of this as a resolution tuning parameter. The smaller the number of topics is set, the more general the bag of words in each topic would be, and the looser the connections between them. The algorithm loops through all of the words in each document, assigning every word to one of our topics in a temporary and semi-random manner. This initial assignment is arbitrary and it is easy to show that different initialisations lead to the same results in long run. Once each word has been assigned a temporary topic, the algorithm then re-iterates through each word in each document to update the topic assignment using two criteria: 1) How prevalent is the word in question across topics? And 2) How prevalent are the…

Social Data Science

Creating a semantic map of sexism worldwide: topic modelling of content from the “Everyday Sexism” project

by taha yasseri 07/10/2015

What are the most common types of sexism globally, and (how) do they relate to each other? Do experiences of sexism change from one country to another?

by taha yasseri 07/10/2015

When barrister Charlotte Proudman recently spoke out regarding a sexist comment that she had received on the professional networking website LinkedIn, hundreds of women praised her actions in highlighting the issue of workplace sexism—and many of them began to tell similar stories of their own. It soon became apparent that Proudman was not alone in experiencing this kind of sexism, a fact further corroborated by Laura Bates of the Everyday Sexism Project, who asserted that workplace harassment is “the most reported kind of incident” on the project’s UK website. Proudman’s experience and Bates’ comments on the number of submissions to her site concerning harassment at work provokes a conversation about the nature of sexism, not only in the UK but also at a global level. We know that since its launch in 2012, the Everyday Sexism Project has received over 100,000 submissions in more than 13 different languages, concerning a variety of topics. But what are these topics? As Bates has stated, in the UK, workplace sexism is the most commonly discussed subject on the website – but is this also the case for the Everyday Sexism sites in France, Japan, or Brazil? What are the most common types of sexism globally, and (how) do they relate to each other? Do experiences of sexism change from one country to another? The multi-lingual reports submitted to the Everyday Sexism project are undoubtedly a gold mine of crowdsourced information with great potential for answering important questions about instances of sexism worldwide, as well as drawing an overall picture of how sexism is experienced in different societies. So far much of the research relating to the Everyday Sexism project has focused on qualitative content analysis, and has been limited to the submissions written in English. Along with Principal Investigators Taha Yasseri and Kathryn Eccles, I will be acting as Research Assistant on a new project funded by the John Fell Oxford University Press…

Governance & Security, Tools

Using Wikipedia as PR is a problem, but our lack of a critical eye is worse

by taha yasseri 04/09/2015

That Wikipedia is used for less-than scrupulously neutral purposes shouldn’t surprise us – our lack of critical eye that’s the real problem.

by taha yasseri 04/09/2015

Reposted from The Conversation. If you heard that a group of people were creating, editing, and maintaining Wikipedia articles related to brands, firms and individuals, you could point out, correctly, that this is the entire point of Wikipedia. It is, after all, the “encyclopedia that anyone can edit”. But a group has been creating and editing articles for money. Wikipedia administrators banned more than 300 suspect accounts involved, but those behind the ring are still unknown. For most Wikipedians, the editors and experts who volunteer their time and effort to develop and maintain the world’s largest encyclopedia for free, this is completely unacceptable. However, what the group was doing was not illegal—although it is prohibited by Wikipedia’s policies—and as it’s extremely hard to detect it’s difficult to stamp out entirely. Conflicts of interest in those editing articles has been part of Wikipedia from the beginning. In the early days, a few of the editors making the most contributions wanted a personal Wikipedia entry, at least as a reward for their contribution to the project. Of course most of these were promptly deleted by the rest of the community for not meeting the notability criteria. As Wikipedia grew and became the number one source of free-to-access information about everything, so Wikipedia entries rose up search engines rankings. Being well-represented on Wikipedia became important for any nation, organisation, firm, political party, entrepreneur, musician, and even scientists. Wikipedians have strived to prohibit self-serving editing, due to the inherent bias that this would introduce. At the same time, “organised” problematic editing developed despite their best efforts. The glossy sheen of public relations The first time I learned of non-Wikipedians taking an organised approach to editing articles I was attending a lecture by an “online reputation manager” in 2012. I didn’t know of her, so I pulled up her Wikipedia entry. It was readily apparent that the article was filled with only positive things. So I did a bit of research about…

Mapping, Social Data Science

After dinner: the best time to create 1.5 million dollars of ground-breaking science

by taha yasseri 24/04/2015

The Zooniverse is a predominant example of citizen science projects that have enjoyed particularly widespread popularity and traction online.

by taha yasseri 24/04/2015

Count this! In celebration of the International Year of Astronomy 2009, NASA's Great Observatories—the Hubble Space Telescope, the Spitzer Space Telescope, and the Chandra X-ray Observatory—collaborated to produce this image of the central region of our Milky Way galaxy. Image: Nasa Marshall Space Flight Center

Since it first launched as a single project called Galaxy Zoo in 2007, the Zooniverse has grown into the world’s largest citizen science platform, with more than 25 science projects and over 1 million registered volunteer citizen scientists. While initially focused on astronomy projects, such as those exploring the surfaces of the moon and the planet Mars, the platform now offers volunteers the opportunity to read and transcribe old ship logs and war diaries, identify animals in nature capture photos, track penguins, listen to whales communicating and map kelp from space. These projects are examples of citizen science; collaborative research undertaken by professional scientists and members of the public. Through these projects, individuals who are not necessarily knowledgeable about or familiar with science can become active participants in knowledge creation (such as in the examples listed in the Chicago Tribune: Want to aid science? You can Zooniverse). The Zooniverse is a predominant example of citizen science projects that have enjoyed particularly widespread popularity and traction online. Although science-public collaborative efforts have long existed, the Zooniverse is a predominant example of citizen science projects that have enjoyed particularly widespread popularity and traction online. In addition to making science more open and accessible, online citizen science accelerates research by leveraging human and computing resources, tapping into rare and diverse pools of expertise, providing informal scientific education and training, motivating individuals to learn more about science, and making science fun and part of everyday life. While online citizen science is a relatively recent phenomenon, it has attracted considerable academic attention. Various studies have been undertaken to examine and understand user behaviour, motivation, and the benefits and implications of different projects for them. For instance, Sauermann and Franzoni’s analysis of seven Zooniverse projects (Solar Stormwatch, Galaxy Zoo Supernovae, Galaxy Zoo Hubble, Moon Zoo, Old Weather, The Milkyway Project, and Planet Hunters) found that 60 percent of volunteers never return to a project after finishing…