The #MeToo movement :

A Temporal, Gender Based and Sentimental Perspective


The aim of this project is to use a dataset of tweets containing #metoo and associated hashtags, in order to analyze this movement and better understand it.

The #MeToo movement is a global movement against sexual abuse. It spread virally in October 2017 as a hashtag used on social media in order to show the widespread phenomenon of sexual harassment, particularly in the workplace. The idea is to fearlessly talk about any sexual abuse one might experience and to rebel against the culture of staying silent after going through such traumatising experiences. A lot of celebrities participated in this movement by highlighting their stories on social media, including renowned actress, personalities, and politics, but also people from simple backgrounds. As a team we are motivated to understand the movement because of its intensity and controverse. It is a challenge to put aside our personal opinion as individuals and consider only the data around this thematic to arrive to an interesting yet objective datastory.

Explore the movement timeline



Let's begin by exploring the movement activity by hashtag, and let's try to correlate the activity peaks with known major events. To do so, we computed the number of tweets containing a given hashtag, for each day. We will assume that the distribution over time of our dataset is representative of the real distribution on Twitter ; which is a reasonable assumption since the tweets have been randomly extracted from Twitter.

The hashtag list has been arbitrarily chosen by us among the most frequent hashtags in our dataset, based on relevance and interest. Thereafter, we looked for the major events in the movement and in the period of time of our dataset. Here is the resulting event list :