51 research outputs found
Violent music vs violence and music: Drill rap and violent crime in London
The current policy of removing drill music videos from social media platforms
such as YouTube remains controversial because it risks conflating the
co-occurrence of drill rap and violence with a causal chain of the two.
Empirically, we revisit the question of whether there is evidence to support
the conjecture that drill music and gang violence are linked. We provide new
empirical insights suggesting that: i) drill music lyrics have not become more
negative over time if anything they have become more positive; ii) individual
drill artists have similar sentiment trajectories to other artists in the drill
genre, and iii) there is no meaningful relationship between drill music and
real-life violence when compared to three kinds of police-recorded violent
crime data in London. We suggest ideas for new work that can help build a
much-needed evidence base around the problem
Examining UK drill music through sentiment trajectory analysis
This paper presents how techniques from natural language processing can be
used to examine the sentiment trajectories of gang-related drill music in the
United Kingdom (UK). This work is important because key public figures are
loosely making controversial linkages between drill music and recent
escalations in youth violence in London. Thus, this paper examines the dynamic
use of sentiment in gang-related drill music lyrics. The findings suggest two
distinct sentiment use patterns and statistical analyses revealed that lyrics
with a markedly positive tone attract more views and engagement on YouTube than
negative ones. Our work provides the first empirical insights into the language
use of London drill music, and it can, therefore, be used in future studies and
by policymakers to help understand the alleged drill-gang nexus
Identifying the sentiment styles of YouTube's vloggers
Vlogs provide a rich public source of data in a novel setting. This paper examined the continuous sentiment styles employed in 27,333 vlogs using a dynamic intra-textual approach to sentiment analysis. Using unsupervised clustering, we identified seven distinct continuous sentiment trajectories characterized by fluctuations of sentiment throughout a vlog's narrative time. We provide a taxonomy of these seven continuous sentiment styles and found that vlogs whose sentiment builds up towards a positive ending are the most prevalent in our sample. Gender was associated with preferences for different continuous sentiment trajectories. This paper discusses the findings with respect to previous work and concludes with an outlook towards possible uses of the corpus, method and findings of this paper for related areas of research
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