Since the shooting of Black teenager Michael Brown by White police officer Darren Wilson in Ferguson, Missouri, the protest hashtag #BlackLivesMatter has amplified critiques of extrajudicial killings of Black Americans. In response to #BlackLivesMatter, other Twitter users have adopted #AllLivesMatter, a counter-protest hashtag whose content argues that equal attention should be given to all lives regardless of race. Through a multi-level analysis of over 860,000 tweets, we study how these protests and counter-protests diverge by quantifying aspects of their discourse. We find that #AllLivesMatter facilitates opposition between #BlackLivesMatter and hashtags such as #PoliceLivesMatter and #BlueLivesMatter in such a way that historically echoes the tension between Black protesters and law enforcement. In addition, we show that a significant portion of #AllLivesMatter use stems from hijacking by #BlackLivesMatter advocates. Beyond simply injecting #AllLivesMatter with #BlackLivesMatter content, these hijackers use the hashtag to directly confront the counter-protest notion of “All lives matter.” Our findings suggest that Black Lives Matter movement was able to grow, exhibit diverse conversations, and avoid derailment on social media by making discussion of counter-protest opinions a central topic of #AllLivesMatter, rather than the movement itself.

Data Availability: The raw tweet data used in this study cannot be released under the Twitter Terms of Agreement. We make available the IDs of all tweets used in this study, which can be used to retrieve the tweets through Twitter’s Public API. These tweet IDs are available on Harvard Dataverse: http://dx.doi.org/10.7910/DVN/IQ525U .

Copyright: © 2018 Gallagher et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Through application of these methods at the word level and topic level, we first show that #AllLivesMatter diverges from #BlackLivesMatter through support of pro-law-enforcement sentiments. This places #BlackLivesMatter and hashtags such as #PoliceLivesMatter and #BlueLivesMatter in opposition, a framing that is in line with the theoretical understanding of #AllLivesMatter and that mimics how relationships between black protesters and law enforcement have been historically depicted. We then demonstrate that #AllLivesMatter experiences significant hijacking from #BlackLivesMatter, while #BlackLivesMatter exhibits rich and informationally diverse conversations, of which hijacking is a much smaller portion. These findings, which augment the theoretical discussion of the impact of the All Lives Matter movement, suggest that the Black Lives Matter movement was able to avoid being derailed by counter-protest opinions on social media by relegating discussion of such opinions to the counter-protest, rather than the movement itself.

Given the popular framing of Black Lives Matter as the New Civil Rights movement, the movement and its oppositions are in and of themselves of interest to study. Furthermore, while qualitative study of #AllLivesMatter has provided a theoretical framing of the hashtag, it is still unclear to what extent, if any, the All Lives Matter movement hijacked the conversations of #BlackLivesMatter. Through the computational analysis of over 860,000 tweets, in this paper we comprehensively quantify the ways in which the protest and counter-protest discourses of #BlackLivesMatter and #AllLivesMatter diverge most significantly. Unlike previous studies of political polarization that focus on hashtag trends or curated lists of terms [ 30 – 33 ], the methods we leverage take advantage of the entirety of textual data, thus giving weight to all protest sentiments that were voiced through these hashtags.

#BlackLivesMatter has found itself contended by a relatively vocal counter-protest hashtag: #AllLivesMatter. Advocates of #AllLivesMatter affirm that equal attention should be given to all lives, while #BlackLivesMatter supporters contend that such a sentiment derails the Black Lives Matter movement. The counter-hashtag #AllLivesMatter has received less attention in terms of research, largely being studied from a theoretical angle. The phrase “All Lives Matter” reflects a “race-neutral” or “color-blind” approach to racial issues [ 26 ]. While this sentiment may be “laudable,” race-neutral attitudes mask power inequalities that result from racial biases [ 27 ]. Thus, those who adopt #AllLivesMatter evade the importance of race in the discussion of Black deaths in police-involved shootings [ 28 , 29 ]. To our knowledge, our work is the first to engage in a data-driven approach to understanding #AllLivesMatter. This approach not only allows us to substantiate several broad claims about the use of #AllLivesMatter, but to also highlight trends in #AllLivesMatter that are absent from the theoretical discussion of the hashtag.

Researchers have only just begun to study the emergence and structure of #BlackLivesMatter and its associated movement. To date and to the best of our knowledge, Freelon et al. have provided the most comprehensive data-driven study of Black Lives Matter [ 20 ]. Their research characterizes the movement through multiple frames and analyzes how Black Lives Matter has evolved as a movement both online and offline. Other researchers have given particular attention to the beginnings of the movement and its relation to the events of Ferguson, Missouri. Jackson and Welles have shown that the initial uptake of #Ferguson, a hashtag that precluded widespread use of #BlackLivesMatter, was due to the early efforts of “citizen journalists” [ 21 ], and Bonilla and Rosa argue that these citizens framed the story of Michael Brown in such a way that facilitated its eventual spreading [ 22 ]. Other related work has attempted to characterize the demographics of #BlackLivesMatter users [ 23 ], how #BlackLivesMatter activists affect systemic political change [ 24 ], and how the movement self-documents itself through Wikipedia [ 25 ].

The protest hashtag #BlackLivesMatter has come to represent a major social movement. The hashtag was started by three women, Alicia Garza, Patrisse Cullors, and Opal Tometi, following the death of Trayvon Martin, a Black teenager who was shot and killed by neighborhood watchman George Zimmerman in February 2012 [ 16 ]. The hashtag was a “call to action” to address anti-Black racism, but it was not until November 2014 when White Ferguson police officer Darren Wilson was not indicted for the shooting of Michael Brown that #BlackLivesMatter saw widespread use. Since then, the hashtag has been used in combination with other hashtags, such as #EricGarner, #FreddieGray, and #SandraBland, to highlight the extrajudicial deaths of other Black Americans. #BlackLivesMatter has organized the conversation surrounding the broader Black Lives Matter movement and activist organization of the same name. Some have likened Black Lives Matter to the New Civil Rights movement [ 17 , 18 ], though the founders reject the comparison and self-describe Black Lives Matter as a ‘human rights’ movement [ 19 ].

Protest movements have a long history of forcing difficult conversations in order to enact social change, and the increasing prominence of social media has allowed these conversations to be shaped in new and complex ways. Indeed, significant attention has been given to how to quantify the dynamics of such social movements. Recent work studying social movements and how they evolve with respect to their causes has focused on Occupy Wall Street [ 1 – 3 ], the Arab Spring [ 4 ], and large-scale protests in Egypt and Spain [ 5 , 6 ]. The network structures of movements have also been leveraged to answer questions about how protest networks facilitate information diffusion [ 7 ], align with geospatial networks [ 8 ], and impact offline activism [ 9 – 11 ]. Both offline and online activists have been shown to be crucial to the formation of protest networks [ 12 , 13 ] and play a critical role in the eventual tipping point of social movements [ 14 , 15 ].

The JSD has the useful property of being bounded between 0 and 1. The JSD is 0 when the texts have exactly the same word distribution, and is 1 when neither text has a single word in common. Furthermore, by the linearity of the JSD we can extract the contribution of an individual word to the overall divergence. The contribution of word i to the JSD is given by (5) where m i is the probability of seeing word i in M. The contribution from word i is 0 if and only if p i = q i . Therefore, if the contribution is nonzero, we can label the contribution to the divergence from word i as coming from text P or Q by determining which of p i or q i is larger.

The Kullback-Leibler divergence is a statistic that assesses the distributional differences between two texts. Given two texts P and Q with a total of n unique words, the Kullback-Leibler divergence between P and Q is defined as (3) where p i and q i are the probabilities of seeing word i in P and Q respectively. However, if there is a single word that appears in one text but not the other, this divergence will be infinitely large. Because such a situation is not unlikely in the context of Twitter, we instead leverage the Jensen-Shannon divergence (JSD) [ 44 ], a smoothed version of the Kullback-Leibler divergence: (4) Here, M is the mixed distribution M = π 1 P + π 2 Q where π 1 and π 2 are weights proportional to the sizes of P and Q such that π 1 + π 2 = 1. The Jensen-Shannon divergence has been previously used in textual analyses that range from the study of language evolution [ 45 , 46 ] to the clustering of millions of documents [ 47 ].

Of the diversity indices, only the Shannon index gives equal weight to both common and rare words [ 43 ]. In addition, even for a fixed diversity index, care must be taken in comparing diversity measures to one another [ 43 ]. In order to make linear comparisons of diversity between texts, we convert the Shannon index to an effective diversity. The effective diversity D of a text T with respect to the Shannon index is given by (2) The expression in Eq 2 is also known as the perplexity of the text. The effective diversity allows us to accurately make statements about the ratio of diversity between two texts. For example, unlike the raw Shannon index, the effective diversity doubles in the situation of comparing texts with n and 2n equally-likely words. We apply the effective diversity in Section 3.3.

A large part of our textual analysis relies on tools from information theory, so we describe these methods here and frame them in the context of the corpus. Given a text with n unique words where the ith word appears with probability p i , the Shannon entropy H encodes ‘unpredictability’ as (1) Shannon’s entropy describes the unpredictability of a body of text, and so, intuitively, we say that a text with higher Shannon’s entropy is less predictable than a text with lower Shannon’s entropy. It can then be useful to think of Shannon’s entropy as a measure of diversity, where high entropy (unpredictability) implies high diversity. In this case, we refer to Shannon’s entropy as the Shannon index. We employ this raw diversity measure in Section 3.1 to examine the diversity of language surrounding individual words.

The plot is annotated with several major events pertaining to the hashtags. Shaded regions indicate one-week periods where use of both #BlackLivesMatter and #AllLivesMatter peaked in frequency. These are the periods we focus on in the present study.

Previous work has emphasized the importance of viewing protest movements through small time scales [ 20 , 21 ]. In addition, we do not attempt to characterize all of the narratives that exist within #BlackLivesMatter and #AllLivesMatter. Therefore, we choose to restrict our analysis to eight one-week periods where there were simultaneous spikes in #BlackLivesMatter and #AllLivesMatter. These one-week periods are labeled on Fig 1 and are as follows:

We collected tweets containing #BlackLivesMatter and #AllLivesMatter (case-insensitive) from the period August 8th, 2014 to August 31st, 2015 from the Twitter Gardenhose feed. The Gardenhose represents a 10% random sample of all public tweets, and our collection of these tweets was in accordance Twitter’s Terms of Service. Our subsample resulted in 767,139 #BlackLivesMatter tweets from 375,620 unique users and 101,498 #AllLivesMatter tweets from 79,753 unique users. Of these tweets, 23,633 of them contained both hashtags. When performing our analyses, these tweets appear in each corpus.

3 Results