Expanding upon our prior research endeavors,
our study delves into the dynamics of communal support
towards migrants and refugees in the wake of
transformative events, focusing specifically on the period
before and after the emergence of the COVID-19
pandemic. We achieve this through the meticulous
collection and examination of an extensive and original
dataset composed of tweets associated with migration.
Our primary objective entails the evaluation of
alterations in social cohesion and solidarity expressed
towards migrants during these distinct temporal phases.
To initiate this investigation, we meticulously assess
a corpus of more than 2000 tweets, deciphering the
presence of either supportive or adverse sentiments
towards immigrants. This is accomplished by employing
two distinct approaches for annotation: one reliant on the
expertise of individuals and the other soliciting
contributions from the general public. Building upon
these annotated tweets, we develop a Long Short-Term
Memory (LSTM) model, enriched by a multitude of data
augmentation strategies. Impressively, the performance
of this model approaches the upper echelons of human
accuracy. This finely-tuned model serves as the
foundation for the subsequent automated labeling of over
240,000 tweets spanning the period from September 2019
to June 2021.
Through a meticulous analysis of these automated
labels, we elucidate the evolving landscape of migrant-
oriented sentiments over this critical period,
encompassing both the prelude and the aftermath of the
COVID-19 outbreak. Notably, our findings underscore
the escalating prominence and contentiousness of
expressions of solidarity towards migrants during the
initial phases of the pandemic. However, as the timeline
progresses, this solidarity seems to recede in significance,
with a slight dip in tweet volume below levels observed in
more standard contexts by the summer of 2021.
Interestingly, a subset of tweets linked to the COVID-19
crisis displays an elevated proportion of sentiments
In a significant contribution to the field, we also
address the potential pitfalls associated with scrutinizing
trends in social cohesion. For instance, we underscore
how the balance between solidarity-affirming and anti-
solidarity sentiments can be influenced by various factors,
such as the choice of sampled tweets, the linguistic
characteristics of these tweets, the national identification
of Twitter users (whether known or anonymous), and the
meticulous selection of pertinent tweets.
Keywords : Solidarity, Crisis, Social Media, Long Short- Term Memory(LSTM), Random Forest, Social Dynamics, Trend Analysis, Societal Responses, Anti-Solidarity.