The Brexit referendum in 2016 led to an increase in hate crimes in the UK of 57% (O`Neill, 2017; Snowdon, 2016) – with no such trends observed in non-aggravated criminal offences. After the victory of the Conservative Party in the UK General Election 2019, hate speech on Twitter increasingly targeted immigrants. And an increasing number of anti-Asian and anti-Semitic hate crimes has been recorded since the recent outbreak of COVID-19. All these examples are illustrations of sharp increases or spikes in hate arising subsequently to a specific event. Understanding the processes behind convicted hate-motivated crimes is crucial since attacks of this nature target fundamental characteristics and/ or non-alterable circumstances and are therefore not only detrimental for the victim but an entire community. However, especially incidents of 'everyday hate' often remain un- or underreported. Inspired by social-psychological research on collective empowerment, we propose that a social identity framework can help to explain how external factors (e.g., national rhetoric, electoral outcomes) lead to ingroup extension (‘xenophobic British Whites’) and outgroup creation (everyone not ‘truly British’) (cf. Drury & Reicher, 1999; Tajfel & Turner, 1986; Turner, Hogg, Oakes, Reicher & Wetherell, 1987; Turner, 1985); through the perception that the in-group´s social identity and xenophobic values are shared by the wider (White) public, the (false) perception of a consensus that hate is socially acceptable, and that hostile actions are supported is established. In this way, xenophobes´ social identity is realized, which can be accompanied by a joyful feeling and in turn increase the likelihood for further hate (cf. Becker, Tausch, & Wagner, 2011; Drury & Reicher, 2005). Only a minority of xenophobes commits hate crimes though. We think that particularly
those individuals that highly identify with their in-group and that show strong
xenophobic attitudes are prone to these processes, as well as to nationalist
rhetoric which stresses the superiority of the in-group. This, in turn, can
lead to intergroup aggression, violence, feelings of injustice and deprivation
de Zavala, Guerra & Simão, 2017; Marchlewska, Cichocka, Panayiotou,
Castellanos & Batayneh, 2018), and potentially to participation in White collective action (cf.,
Sternisko, Cichocka & Van Bavel, 2020).To examine how not only to prevent but to also to reverse xenophobic actions, the project follows a mixed-methods approach, considering the longitudinal examination of social norm (mis) perception, hate forum observations, semi-structured interviews with witnesses of hate-motivated attacks, and investigations of the patterns and predictors of hate crimes over a specific time. For the latter, we collaborate with the Metropolitan Police Service, London. We requested non-personal data of a period of three years, capturing hate crime details such as offence location, offenders´ residential area, the target group, and the kind of conducted crime. This allows us to compare characteristics of spikes emerging after political events, after incidents that pose threats to public safety (i.e., terror attacks), and characteristics of quieter periods. This will give us greater confidence in predicting where such rises are likely to occur, as well as what the different forms and targets are.Advisory agencies and authorities (e.g., the Commission for Countering Extremism and police forces) concerned with public safety and order can benefit from the outcomes of this research since enhanced knowledge about selective hostility as a reaction to events of different nature allows greater efficacy in prevention and tackling of public disorders. Greater perceived and observed efficacy can also encourage witnesses and victims of hate crimes to report incidents. Therefore, a public picture of authorities increasingly and more efficiently persecuting hate crimes, and enhanced reporting can help to counteract the rise of hate crimes in the first place.
For the time between 01.01.2015 to 31.12.2017 we requested non-personal data of the following:
Monthly and daily recordings of hate crimes based on racial and religious offence motivation, i.e., all recorded attacks perceived by the victim(s) as based on prejudice against race or ethnicity, religion, or beliefs.
Information about the target group, e.g., crime against the Muslim, Black, Polish community, etc.
The crime category (hostility against the person vs property).
The borough (since no personal data is requested, the locations are based on the first four digits of the postcode only)
The analysis will include
A time-series analysis of monthly data for a period of three years (2015 to 2017) will be applied to display the overall development. Time-series analysis for daily data will then focus on times around the events of interest (2016: Brexit referendum; 2017: Terror attacks).
Descriptive statistics for their occurrence divided by year An overview of hate crimes occurrence and crime category will be presented in the form as the total for each provided location (based on the first four digits of the postcode) and averages per year. This will be further broken down to the number of attacks on specific target groups and displayed as a total in each location (first four digits of the postcode) and averages per year.
Descriptive comparisons between three different time points, and hierarchical regressions of characteristics of three different times on the number of hate crimes
Descriptive comparisons of (a) a quieter period of no spikes vs spikes (2016, 2017); and (b) the spikes in 2016 vs 2017 will be conducted. The comparison will refer to differences in location characteristics (based on the first four digits of the postcode) of offence area and residential area of the offender. The independent variables thereby are demographics (gender, age, ethnicity), retrieved from the Census 2011; relative deprivation ranking (Index of Multiple Deprivation, IMD), retrieved from the Office of National Statistics (ONS); and referendum results (only applicable for the spike in 2016), i.e., whether the area was dominated by Leave or Remain voters (based on data per borough), retrieved from the Electoral Commission. For each spike and the dip period, we will select a data frame (e.g., five days). Additionally, we will assess the crime category and target group for each period. Information about the target group will be supplemented with anecdotal data (e.g., newspaper articles
and social media), and Civil Society Organizations (e.g., TellMAMAUK, CST). Finally, we will statistically compare the three time periods by running hierarchical regressions of location characteristics on the number of hate crimes.