The increased participation of women in politics not only in government but as active voters, has contributed by tipping the balance in favor of those representing their interests at best. The last two democratic presidential winners, for example, saw a higher turnout of female voters than male voters, contributing to their success. This trend however, has been captured by those creating narratives around the political campaigns and used dishonestly by co-opting the language of feminist movements to further agendas that may indirectly or directly, promote gender inequality. A phenomenon referred to as “Purple Washing”. The argument surrounding this problem lies in the need to implement language recognition models able to identify the co-opting of feminine language in the political discourse, allowing for the dissemination of a more transparent ideology. As well as ensuring factors as diversity in the data sets, utilizing both quantitative and qualitative methods, and examining intersectionality across gender, race, class and sexuality are included, in order to reducing bias and securing that data is understood within a contextual framework. While the risks of perpetuating biases with the use of computerized tools needs to be acknowledged, there are also opportunities that may prove crucial at cutting ties with traditional politics and at challenging male dominated-structures. 

Historical reference

Twitter was created in 2006 and since then it has transformed the way in which we communicate and engage with others. In 2007 Chris Messina suggested using the symbol “#” hashtag for the first time on Twitter, in order to organize and categorize related posts. The hashtag became an integral part of social media and then went on to spark some of the biggest political and social movements in recent years. In 2009 the hashtag #Iranelection became the first international-trending hashtag. The term “hashtag activism” appeared in 2011 in news reports to describe online activism with the hashtag branding. As of February 9, 2023, Twitter announced that it won’t be allowing access to its API. Currently only people with active accounts can still use the API…

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How can feminist activism use social media as a tool to challenge and reshape power structures and advocate for justice? In the book Hashtag activism the authors investigate some of the most relevant twitter hashtags addressing women’s violence, culture dynamics, discrimination of the trans community and racism. The authors focus on marginal communities that brought social issues situated on the periphery to a more central realm with the use of the hashtag, achieving a “considerable impact on political debates, social policy, and the public consciousness”. The creation of counterpublic spaces for the marginalized was seen then, as a win for inclusivity and visibility, in which whole networks benefited. 

The author’s intend not only to analyze popular hashtags individually like #metoo or #sayhername, but understanding the network activity between those tweeting about race, sexuality, and gender. They argue that “transforming online counterpublics into network data makes it possible to understand their dynamic and interdependent properties while also narrowing an overwhelming volume of data to a manageable size”. Allowing them to find patterns in how tweets achieve popularity and identifying which individuals within the networks become the main actors. Interestingly, each chapter contains an essay written by one influential person linked to the hashtags the authors selected.

The way in which the authors construct the networks is through the analysis of retweets and mentions (@). Once the network reaches a legible shape, the authors identify individuals, messages and subgroups of interest. They argue that their research benefits from using a mathematical agnostic approach, which relies on quantitative measures and statistical algorithms to identify relationships within a text and doesn’t require the researcher to have a preconceived notion, but just lets the data speak for itself. In addition, the authors investigated the mathematical properties of degree centrality and betweenness centrality, for network creation. Degree centrality measures the direct relationships between two nodes while the betweenness centrality is the measure of how often a node acts as a bridge. 

In order to reconcile digital tools and humanities, the authors employ Stephen Ramsey’s construct of algorithm criticism, which stresses the need to treat algorithms as cultural artifacts, set in social and political environments, to critically examine pre-assumptions and biases. Other methods, as feminist and critical race theories were also applied to investigate power and identity within the corpus. Finally, discourse analysis permitted the researchers to find meaning and significance beyond the popularity of certain hashtags and the social context in which these flourished to form attentional sets of networks. 

The first three chapters of the book explore women’s violence, black feminism and trans feminist advocacy, represented by trending Twitter hashtags like #yesallwomen #metoo, #sayhername, #youoksis and #girlslikeus. These hashtags have been effective at piercing the social media sphere by creating awareness and mobilizing the general public. The use of hashtags by social justice movements not only provided a means of communication but an opportunity to demand change, and influence the discourse in media news and political agendas.  

As computational methods are often used to study social media and identify patterns and trends, it is of extreme importance to understand the limitations and potential biases of each model as Richard Jean explains in his article “All Models are Wrong”. In contrast with this idea the authors used a series of methods to reduce bias, like agnostic modeling. However, agnostic models could in theory carry out mistakes as well if the data collected was incomplete or had already some form of bias. The authors recognize that text analysis is never perfect due to issues such as limitations at identifying relevant tweets, bias at analyzing only english-speaking populations instead of the global manifest, lack of context caused by the set number of characters and brevity of Twitter posts, or the potential for misinterpretation of sarcasm and irony in language. 

I consider that the authors were successful at taking a critical stand in their research and recognizing the limitations of it. One of their biggest contributions lies in the act of inviting the main voices representing the hashtags and correlating their experiences with their findings. This approach generates an extra layer of dimensionality that speaks to the social relevance of their work. Centering marginalized voices in the development and use of digital tools for social change as D’Ignazio stresses in her book Data Feminism. Another successful approach was not only to perform computerized data analysis but matching these to qualitative analysis, that contextualize and inform the first, offering a comprehensive view of the intersection between technology and social justice.

Yes, #hashtagactivism can suffer from the co-optation of corporate interests, the potential for online harassment and abuse, and even the limitations of achieving a wholesome sustained activism. But by exploring examples of successful instances in which the digital message permeates to the public to cause action and restructure frameworks of power, we can gain a deeper understanding of hashtag activism on social change. For now we’ll just have to work together to mitigate and advocate against the negative impact that modern technologies may bring.