A research team at the OII has already been analysing the content, and provided cleaned data to the datahack participants that could be analysed through qualitative and quantitative methods. Following an introduction to the project by Laura Bates, an outline of the dataset by Taha Yasseri, and a speed-networking session led by Kathryn Eccles we fell into two teams to work with the data.

Our own group wanted to examine the question of how people interact with the threat of public space. We were also interested in how public space is divided between online and offline, and the social perception of being online versus offline. We wanted to explore what sorts of reactions people might have to examples of assault, or strategies or things they might do in response to something happening to them — and how they might differ online and offline.

We spent the first hour collecting keywords that might indicate reactions to either online or offline harassment, including identifying a perceived threat and coping with it. We then searched the raw data for responses like “I tried to ignore it” “I felt safe / unsafe” “I identified a risk” “I was feeling worried, feeling anxious or nervous“; and also looked at online versus offline actions. So for online action we were looking for specific platforms being named, and people saying things like “comment, response, delete, remove” in relation to social media posts. For offline we were looking for things like “I carried a [specific item]” or “I hid or avoided certain areas“ or “I walked faster” (etc.).

We wanted to know if we could apply ideas of responses to offline space back to online spaces, and how these online spaces fall short. Offline responses are often very individual, whereas you might not have such a direct and individual response to something like a Facebook ad. Taking into account the important caveat that this was just a quick exploration of the data — and that the data were indicative rather than representative (so should in no way be used to extrapolate or infer anything concrete) one of the biggest things we found was that while in the offline examples of responses to harassment there was quite a lot of action, like running away, or hiding in shops and restaurants, there were very few examples to responses in the online examples.

Though it actually turned out to be difficult to identify a clear division between online/offline contexts in the data: we saw accounts of people who were online on social media encountering something sexist and logging off, and then walking in the street and getting harassed. But it seemed like people were more likely to report something offline to the police than in online forums. And this contrast is very interesting, in terms of whether you can be an active agent in response to something, or whether there’s something about being online that positions you as being passive and unable to respond — and what we can do about that.

While we found it difficult to quantify, we did wonder if people might not be giving themselves credit for the kinds of responses they have to examples of sexism online — maybe they aren’t thinking about what they do. Whereas offline they might say “I ran away, because I was so scared” perhaps when it’s online, people just read it and not respond; or at least not report responses to the same extent. There were lots of complaints about images, or hypocrisy about Facebook’s enforcement of community standards (such as allowing rape jokes, but deleting pictures of breast-feeding), and other things like that. But the accounts don’t say if they reported it or took action.

This is strange because in cases of offline harassment in the street, where it escalates into something physical like a fight, women are often at a disadvantage: whereas in the online context women ought to have more leverage — but it does’t seem like reporting is being done. When we examined the themes of how people reacted online, we further differentiated between removing the source of a sexist comment (such as unfriending, unfollowing, muting, deleting) and removing the self (such as going offline, or removing yourself from the platform). It seemed that removing the source was generally more common than removing the self.

So people might simply be normalising the idea that misogyny and sexism is going to exist in forums. In the data someone had reported someone on Twitter saying “Well if you’re on Twitter you’re asking for it” — indicative of a “short-skirt” line of thinking about engaging on social media. In this environment people might see unfollowing and unfriending as a form of management and negotiation, as opposed to a fundamental problem with the site itself. It would be interesting to explore the self-censoring that happens before anything happens: quite a few of the examples we read opened with “I wasn’t even wearing anything provocative, but [this] happened..”. And it would be interesting to know if people also think like that in the online context: “I wasn’t even participating in a controversial way, but this still happened”. It’s an interesting parallel, maybe.

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 contextualizes 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 categorization” (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 Kay (2005) have highlighted the damaging intellectual and mental health outcomes for women who are subject to continual experiences of sexism. Atwood, for example, argues in her study of gender bias in families, that sexism combines with other life stressors to create significant psychological distress in women, resulting in them needing to “seek therapy, most commonly for depression and anxiety” (2001, 169).

Given its increasing ubiquity in every day life, it is hardly surprising that the relationship between technology and sexism has also sparked interest from contemporary researchers in the field. Indeed, several studies have explored the intersection between gender and power online, with Susan Herring’s work on gender differences in computer-mediated communication being of particular note (cf. Herring, 2008). Theorists such as Mindi D. Foster have focused on the impact that using digital technology, and particularly Web 2.0 technologies, to talk about sexism can have on women’s well being. Foster’s study found that when women tweeted about sexism, and in particular when they used tweets to a) name the problem, b) criticise it, or c) to suggest change, they viewed their actions as effective and had enhanced life satisfaction, and therefore felt empowered (Foster, 2015: 21).

Despite this diversity of research on sexism, however, there remain some notable gaps in understanding. In particular, as this study hopes to highlight, little previous research on sexism has considered the different ‘types’ of sexism experienced by women (beyond an identification of the workplace and the education system as contexts in which sexism often manifests as per Barnett, 2005; Watkins et al., 2006; Klein, 1992). Furthermore, research focusing on sexism has thus far been largely qualitative in nature. Although a small number of studies have employed quantitative methods (cf. Brandt 2011; Becker and Wright, 2011), none have used computational approaches to analyse the wealth of available online data on sexism.

This project, which will apply a natural language processing approach to analyse data collected from the Everyday Sexism Project website, seeks to fill such a gap. By providing much needed analysis of a large-scale crowd sourced data set on sexism, it is our hope that knowledge gained from this study will advance both the sociological understanding of women’s lived experiences of sexism, and methodological understandings of the suitability of computational topic modelling for conducting this kind of research.

Find out more about the OII’s research on the Everyday Sexism project by visiting the webpage or by looking at the other Policy & Internet blog posts on the project – post 1 and post 2.

Taha Yasseri is a Research Fellow at the OII who has interests in analysis of Big Data to understand human dynamics, government-society interactions, mass collaboration, and opinion dynamics.

Kathryn Eccles is a Research Fellow at the OII who has research interests in the impact of new technologies on scholarly behaviour and research, particularly in the Humanities.

Sophie Melville is a Research Assistant working at the OII. She previously completed the MSc in the Social Science of the Internet at the OII.

References

Atwood, N. C. (2001). Gender bias in families and its clinical implications for women. Social Work, 46 pp. 23–36.

Barnett, R. C. (2005). Ageism and Sexism in the workplace. Generations. 29(3) pp. 25 30.

Bates, Laura. (2015). Everyday Sexism [online] Available at: http://everydaysexism.com [Accessed 1 May 2016].

Becker, Julia C. & Wright, Stephen C. (2011). Yet another dark side of chivalry: Benevolent sexism undermines and hostile sexism motivates collective action for social change. Journal of Personality and Social Psychology, Vol 101(1), Jul 2011, 62-77

Brandt, Mark. (2011). Sexism and Gender Inequality across 57 societies. Psychiological Science. 22(11).

Chodorow, Nancy (1995). “Becoming a feminist foremother”. In Phyllis Chesler, Esther D. Rothblum, Ellen Cole,. Feminist foremothers in women’s studies, psychology, and mental health. New York: Haworth Press. pp. 141–154.

De Beauvoir, Simone. (1949). The second sex, woman as other. London: Vintage.

Foster, M. D. (2015). Tweeting about sexism: The well-being benefits of a social media collective action. British Journal of Social Psychology.

Friedan, Betty. (1963). The Feminine Mystique. W. W. Norton and Co.

Glick, Peter. & Fiske, Susan T. (1997). Hostile and Benevolent Sexism. Psychology of Women Quarterly, 21(1) pp. 119 – 135.

Harper, Amney J. (2008). The relationship between sexism, ambivalent sexism, and relationship quality in heterosexual women. PhD Auburn University.

Herring, Susan C. (2008). Gender and Power in Online Communication. In Janet Holmes and Miriam Meyerhoff (eds) The Handbook of Language and Gender. Oxford: Blackwell.

Jost, J. T., & Kay, A. C. (2005). Exposure to benevolent sexism and complementary gender stereotypes: Consequences for specific and diffuse forms of system justification. Journal of Personality and Social Psychology, 88 pp. 498–509.

Klein, Susan Shurberg. (1992). Sex equity and sexuality in education: breaking the barriers. State University of New York Press.

Matteson, A. V., & Moradi, B. (2005). Examining the structure of the schedule of sexist events: Replication and extension. Psychology of Women Quarterly, 29 pp. 47–57.

McRobbie, Angela (2004). Post-feminism and popular culture. Feminist Media Studies, 4(3) pp. 255 – 264.

Millett, Kate. (1971). Sexual politics. UK: Virago.

Modleski, Tania. (1991). Feminism without women: culture and criticism in a “postfeminist” age. New York: Routledge.

Renzetti, C. and D. Curran, 1992, “Sex-Role Socialization”, in Feminist Philosophies, J. Kourany, J. Sterba, and R. Tong (eds.), New Jersey: Prentice Hall.

Rubin, Gayle. (1975). The traffic in women: notes on the “political economy” of sex. In Rayna R. Reiter (ed.), Toward and anthropology of women. Monthly Review Press.

Swim, J. K., Hyers, L. L., Cohen, L. L., & Ferguson, M. J. (2001). Everyday sexism: Evidence for its incidence, nature, and psychological impact from three daily diary studies. Journal of Social Issues, 57 pp. 31–53.

Watkins et al. (2006). Does it pay to be sexist? The relationship between modern sexism and career outcomes. Journal of Vocational Behaviour. 69(3) pp. 524 – 537.

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 initializations 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 topics in the document?

To quantify these two, the algorithm calculates the likelihood of the words appearing in each document assuming the assignment of words to topics and topics to documents. 

Of course words can appear in different topics and more than one topic can appear in a document. But the iterative algorithm seeks to maximize the self-consistency of the assignment by maximizing the likelihood of the observed word-document statistics. 

We can illustrate this process and its outcome by going back to our example. A topic modelling approach might use the process above to discover the following topics across our documents:

• Document 1: I like to eat kippers for breakfast. [100% Topic A]
• Document 2: I love all animals, but kittens are the cutest. [100% Topic B]
• Document 3: My kitten eats kippers too. [67% Topic A, 33% Topic B]

Topic modelling defines each topic as a so-called ‘bag of words’, but it is the researcher’s responsibility to decide upon an appropriate label for each topic based on their understanding of language and context. Going back to our example, the algorithm might classify the underlined words under Topic A, which we could then label as ‘food’ based on our understanding of what the words mean. Similarly the italicised words might be classified under a separate topic, Topic B, which we could label ‘animals’. In this simple example the word “eat” has appeared in a sentence dominated by Topic A, but also in a sentence with some association to Topic B. Therefore it can also be seen as a connector of the two topics. Of course animals eat too and they like food!

We are going to use a similar approach to first extract the main topics reflected on the reports to the Everyday Sexism Project website and extract the relation between the sexism-related topics and concepts based on the overlap between the bags of words of each topic. Finally we can also look into the co-appearance of topics in the same document.  This way we try to draw a linguistic picture of the more than 100,000 submitted reports.

As ever, be sure to check back for further updates on our progress!

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 Research Fund, that hopes to expand the methods used to investigate Everyday Sexism submission data, by undertaking a large-scale computational study that will enrich existing qualitative work in this area.

Entitled “Semantic Mapping of Sexism: Topic Modelling of Everyday Sexism Content”, our project will take a Natural Language Processing approach, analysing the content of Everyday Sexism reports in different languages, and using topic-modelling techniques to extract the most commonly occurring sexism-related topics and concepts from the submissions. We will map the semantic relations between those topics within and across different languages, comparing and contrasting the ways in which sexism is experienced in everyday life in different cultures and geographies. Ultimately, we hope to create the first data-driven map of sexism on a global scale, forming a solid framework for future studies in growing fields such as online inequality, cyber bullying, and social well being.

We’re very excited about the project and will be charting our progress via the Policy and Internet Blog, so make sure to check back for further updates!