Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter

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

Beyond the hashtags: #Ferguson, #Blacklivesmatter, and the online struggle for offline justice

In 2014, a dedicated activist movement--Black Lives Matter (BLM)--ignited an urgent national conversation about police killings of unarmed Black citizens. Online tools have been anecdotally credited as critical in this effort, but researchers are only beginning to evaluate this claim. This research report examines the movement's uses of online media in 2014 and 2015. To do so, we analyze three types of data: 40.8 million tweets, over 100,000 web links, and 40 interviews of BLM activists and allies

#BLM Insurgent Discourse, White Structures of Feeling and the Fate of the 2020 "Racial Awakening"

Working with Twitter data, this paper offers new findings on the #BlackLivesMatter movement and "racial awakening" of summer 2020. Framing methods to address this important moment, this paper contends that cultural studies and critical race studies can be enriched through an engagement with new computational approaches. We analyze how white and racial minority voices talked about race and track their fraught contestation for leadership of racial discourse over the summer of 2020. We uncover a surprising story of white colorblindness even in the midst of a "racial awakening," a story that questions claims that the Trump presidency and the summer of 2020 ushered in a new era of US racial consciousness. And we show how a Black and minority discourse with transformative potential surged and receded. For cultural studies, our data and analysis revise Raymond Williams's influential model of cultural evolution by introducing a new concept: the insurgent, a long-building minority cultural strain that surges to contest the dominant culture in a moment of crisis. For critical race studies, our findings revise prominent theorizations of colorblindness, racial ideology, and hegemony. By revealing the messy and unconscious feelings characterizing colorblindness, our data contest theorizations of colorblindness as an ideology and counter the focus on articulate beliefs in theories of racial hegemony. Ultimately, this paper shows that bringing data methods focused on moments of cultural contestation and mass communication into dialogue with field-specific theory and qualitative analyses can expand our models of how race, discourse, and culture operate

Black and Blue: Competing Social Constructions of Police On Instagram and Twitter

Mass media outlets newspapers and television were traditionally where individuals gathered their news information; however, with the growth of new media platforms like Twitter, Facebook, and Instagram, individuals are now co-producers of the content that is seen by the public. Previous research indicated that media-generated images of the police influence public perception and that new media outlets are becoming increasingly influential, particularly in regards to social and political conflicts. This means that research on the role of new media outlets in socially constructing reality is essential, though not much of this research has yet been completed. This current analysis fills this gap in the literature by examining the question of how images of police are constructed by different social groups, using ethnographic content analysis on the social media platforms Instagram and Twitter in relation to police images with the #BlackLivesMatter and #BlueLivesMatter hashtags

Language in Our Time: An Empirical Analysis of Hashtags

Hashtags in online social networks have gained tremendous popularity during the past five years. The resulting large quantity of data has provided a new lens into modern society. Previously, researchers mainly rely on data collected from Twitter to study either a certain type of hashtags or a certain property of hashtags. In this paper, we perform the first large-scale empirical analysis of hashtags shared on Instagram, the major platform for hashtag-sharing. We study hashtags from three different dimensions including the temporal-spatial dimension, the semantic dimension, and the social dimension. Extensive experiments performed on three large-scale datasets with more than 7 million hashtags in total provide a series of interesting observations. First, we show that the temporal patterns of hashtags can be categorized into four different clusters, and people tend to share fewer hashtags at certain places and more hashtags at others. Second, we observe that a non-negligible proportion of hashtags exhibit large semantic displacement. We demonstrate hashtags that are more uniformly shared among users, as quantified by the proposed hashtag entropy, are less prone to semantic displacement. In the end, we propose a bipartite graph embedding model to summarize users' hashtag profiles, and rely on these profiles to perform friendship prediction. Evaluation results show that our approach achieves an effective prediction with AUC (area under the ROC curve) above 0.8 which demonstrates the strong social signals possessed in hashtags.Comment: WWW 201

Exploring the Association Between How Social Media Affects Attitudes Toward Marijuana Legalization

As support for marijuana legalization continues to rise, it’s important to explore how the use of social media may be affecting the attitudes of Americans around this controversial topic. Social media has become a part of everyday life for most as it allows easy communication to anyone anywhere and allows the exchanging of influential ideas over a broad range of topics, especially marijuana legalization. As such, this study utilized data from the 2016 General Social Survey to examine the relationship between how the general use of social media affects attitudes toward marijuana legalization. The findings of this study suggest that social media use was associated with affecting support for marijuana legalization and that more time spent on social media was also associated with supporting marijuana legalization