Thursday, 24 June 2021

In the name of the law, stop the disrespect!

The killing of George Floyd in Minneapolis USA in May 2020 raised questions about the impartiality of police officers when dealing with the public and particularly those from black and ethnic minorities.  Six years earlier, in 2014, a team of researchers from Stanford University investigated exactly this issue by using footage from police body-worn cameras to analyse the language they used during routine traffic stops in the multiethnic city of Oakland in California. 

The researchers transcribed footage from 981 traffic stops of both black and white drivers, which were conducted by 245 different officers during a period of one month. Participants in the study were given transcripts of these interactions without knowing the race, age or sex of the drivers. They were then asked to rate the respect shown by the officers through placing their language on a gradient, showing how respectful, polite, friendly and formal they were. The research team used a model based on linguistic theories of respect where respectful language includes apologising, being grateful, expressing concern for the other person and softening commands to reduce confrontation. 

The results demonstrate that, although officers used the same levels of formality for both black and white drivers, they were rated as significantly less respectful, polite or friendly to black drivers than they were to whites.  Even after controlling for the severity of the traffic offence, the length and outcome of the stop and the race of the officers themselves, interactions with white drivers were consistently more respectful. In fact, 57% of white drivers were more likely to hear an officer say one of the most respectful phrases (e.g. “sir”, “thank you”) in the transcripts whereas black drivers were 61% more likely to hear one of the least respectful (e.g. “hands on the wheel”, or use of first name). 

The researchers conclude that the racial disparities in their study are clear, however the causes of them are not.  They write that these disparities could have far-reaching effects as personal interactions with the police build a community’s opinion about them and ultimately lead to a relationship of trust or distrust. They suggest that future research could expand body camera footage beyond just text to audio features such as intonation and video features such as facial expression, to try and investigate how interactions progress and sometimes break down.  This could be invaluable in informing police officer training and to establish better relationships with the communities they serve. 

Voigt, Rob, Nicholas Camp, Vinodkumar Prabhakaran, William Hamilton, Rebecca Hetey, Camilla Griffiths, David Jurgens, Dan Jurafsky, and Jennifer Eberhardt. 2017. Language from police body camera footage shows racial disparities in officer respect. PNAS 114 (25): 6521-6526.

This summary was written by Gemma Stoyle


Thursday, 27 May 2021


The past year has affected all of us in one way or another but have you ever thought about what effect it may have had on our language?  Philipp Wicke and Mariana Bolognesi did just that in their study of thousands of tweets posted during March and April 2020.

Due to social distancing measures, people were quick to use social media platforms like Twitter to connect with others and express their feelings, sending around 16,000 tweets an hour with hashtags like #coronavirus, #Covid-19 and #Covid. The researchers wanted to explore this online discourse and were particularly interested in how the pandemic was discussed using the metaphor of war. Discourse about disease has often been found to use this metaphor and cancer patients frequently complain that they are described as being in a 'battle' with the illness, which they find negative and unhelpful. With this in mind, Wicke and Bolognesi decided to also explore other figurative ways in which Covid was being described.

They collected 25,000 tweets a day that contained at least one of eight covid-related hashtags. Retweets were not included nor were more than one tweet per user in order to gain a balanced view of language use. 5.32% of the collected tweets mentioned war, the most common words being 'fight' (29.76% of these mentions) and 'war' (10.08%), whilst 'combat', 'threat' and 'battle' were also prevalent. The researchers noted that this could reflect this early stage of the pandemic: it was a global emergency and urgent action was needed to confront the situation. Most of these examples referred specifically to the treatment of the virus and the 'frontline' workers dealing with its effects in hospital.

When they concentrated on other figurative ways in which Covid was being described they found it referred to in terms of a storm, a monster and a tsunami.  For example, the idea of the virus as a storm arose in 1.49% of the tweets and contained words like 'thunderstorm', 'rain' and 'lightning'; 1.13% of the tweets referred to a tsunami, using words like 'earthquake', 'disaster' and 'tide' and references to a monster occurred in 0.68% of the tweets with 'freak', 'demon' and 'devil' being prime examples. These negative images mainly referred to the onset and spread of the virus. It is clear, however, that the war metaphor was used significantly more than these others.

Wicke and Bolognesi conclude that their results confirm previous findings that the war metaphor is common in public discourse of disease; however, they found that it was used very particularly during the first weeks of the pandemic to refer to the initial medical response to it. They also suggest that all of these metaphors are negative and unhelpful and propose the construction of a 'Metaphor Menu', previously suggested with regards to cancer, to give the public more positive and desirable ways to talk about Covid 19 as the pandemic evolves and changes.


Wicke P., M. M. Bolognesi (2020) Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter. PLoS ONE 15(9): e0240010.

This summary was written by Gemma Stoyle