The refugee/migrant crisis dichotomy on twitter: A network and sentiment perspective

Media reports, political statements, and social media debates on the refugee/migrant crisis shape the ways in which people and societies respond to those displaced people arriving at their borders world wide. These current events are framed and experienced as a crisis, entering the media, capturing worldwide political attention, and producing diverse and contradictory discourses and responses. The labels “migrant” and “refugee” are frequently distinguished and conflated in traditional as well as social media when describing the same groups of people. In this paper, we focus on the simultaneous struggle over meaning, legitimization, and power in representations of the refugee crisis, through the specific lens of Twitter. The 369,485 tweets analyzed in this paper cover two days after a picture of Alan Kurdi - a three-year-old Syrian boy who drowned in the Mediterranean Sea while trying to reach Europe with his family - made global headlines and sparked wide media engagement. More specifically, we investigate the existence of the dichotomy between the “deserving” refugee versus the “undeserving” migrant, as well as the relationship between sentiment expressed in tweets, their influence, and the popularity of Twitter users involved in this dichotomous characterization of the crisis. Our results show that the Twitter debate was predominantly focused on refugee related hashtags and that those tweets containing such hashtags were more positive in tone. Furthermore, we find that popular Twitter users as well as popular tweets are characterized by less emotional intensity and slightly less positivity in the debate, contrary to prior expectations. Co-occurrence networks expose the structure underlying hashtag usage and reveal a refugee-centric core of meaning, yet divergent goals of some prominent users. As social media become increasingly prominent venues for debate over a crisis, how and why people express their opinions offer valuable insights into the nature and direction of these debates

Exploring Sentiment Analysis on Twitter: Investigating Public Opinion on Migration in Brazil from 2015 to 2020

openTechnology has reshaped societal interaction and the expression of opinions. Migration is a prominent trend, and analysing social media discussions provides insights into societal perspectives. This thesis explores how events between 2015 and 2020 impacted Brazilian sentiment on Twitter about migrants and refugees. Its aim was to uncover the influence of key sociopolitical events on public sentiment, clarifying how these echoed in the digital realm. Four key objectives guided this research: (a) understanding public opinions on migrants and refugees, (b) investigating how events influenced Twitter sentiment, (c) identifying terms used in migration-related tweets, and (d) tracking sentiment shifts, especially concerning changes in government. Sentiment analysis using VADER (Valence Aware Dictionary and sEntiment Reasoner) was employed to analyse tweet data. The use of computational methods in social sciences is gaining traction, yet no analysis has been conducted before to understand the sentiments of the Brazilian population regarding migration. The analysis underscored Twitter's role in reflecting and shaping public discourse, offering insights into how major events influenced discussions on migration. In conclusion, this study illuminated the landscape of Brazilian sentiment on migration, emphasizing the significance of innovative social media analysis methodologies for policymaking and societal inclusivity in the digital age

Refugee or migrant crisis?

In recent years, increasing attention has been dedicated to the hazardous and volatile situation in the Middle East, a crisis which has pushed many to flee their countries and seek refuge in neighboring countries or in Europe. In describing or discussing these tragic events, labels such as “European migrant crisis” and “European refugee crisis” started being widely used by the media, politicians, and the online world alike. The use of such labels has the potential to dictate the ways in which displaced people are received and perceived. With this study, we investigate label use in social media (specifically YouTube), the emergent patterns of labeling that can cause further disaffection and tension or elicit sympathy, and the sentiments associated with the different labels. Our findings suggest that migration issues are being framed not only through labels characterizing the crisis but also by their describing the individuals themselves. Using topic modeling and sentiment analysis jointly, our study offers valuable insights into the direction of public sentiment and the nature of discussions surrounding this significant societal crisis, as well as the nature of online opinion sharing. We conclude by proposing a four-dimensional model of label interpretation in relation to sentiment—that accounts for perceived agency, economic cost, permanence, and threat, and identifies threat and agency to be most impactful. This perspective reveals important influential aspects of labels and frames that may shape online public opinion and alter attitudes toward those directly affected by the crisis

Social Media, Journalism and Crisis: Twitter Representation of #SyrianRefugees in Western News Media

Generally, there have been conflicts in the world regarding media coverage especially on #SyrianRefugees in Western media. It is undebatable that the Western states have political stability and peace; thus, they remain better hosts for asylum seekers and other refugees who come in search of greener pastures. However, current trends have hindered such countries from being ideal hubs because citizens have basic fears including those related to national security. Refugee situations have attracted lots of controversies over the years to the point that the concern is evident in the media. This dissertation explores the crisis of Syrian refugees and the unending arguments associated with conflicts as seen in #SyrianRefugees. Technology has fueled a number of perspectives on social media platforms where most people react to posts and tweets. Media organizations such as CNN indicated that Syrian refugees were not embraced. For instance, only five states in the United States showed interest in housing these refugees while the rest demonstrated reluctance. Twitter users opposed various moves made to house refugees and research has it that these claims are shared by people as the representation of their home governments. The Syrian crisis was debated widely on social media and especially on Twitter. Global sparks were evident on Twitter when a number of people drowned in the Mediterranean Sea attempting to reach Europe for settlement. Such dangers for refugees were tweeted in an attempt to touch the hearts of governments and human organizations across the world. In response, the claims reached relevant authorities such as the EU who signed treaties with Turkey to accommodate Syrians. Overall, the Syrian crisis was covered widely on Twitter

The Alt-Right and Global Information Warfare

The Alt-Right is a neo-fascist white supremacist movement that is involved in violent extremism and shows signs of engagement in extensive disinformation campaigns. Using social media data mining, this study develops a deeper understanding of such targeted disinformation campaigns and the ways they spread. It also adds to the available literature on the endogenous and exogenous influences within the US far right, as well as motivating factors that drive disinformation campaigns, such as geopolitical strategy. This study is to be taken as a preliminary analysis to indicate future methods and follow-on research that will help develop an integrated approach to understanding the strategies and associations of the modern fascist movement.Comment: Presented and published through IEEE 2019 Big Data Conferenc

The Perceptions of Migration During the Pandemic: What Twitter Data Tell Us?

This paper aims to analyse sentiments and emotions about migration in Italy using Twitter, by comparing the period of COVID-19 pandemic with the previous year. We take Italy as a case study because it has been severely affected by the COVID-19, it is one of the largest recipients of immigrants in Europe and, is among the few countries that implemented an amnesty for irregular migrant workers during the pandemic. We apply a text mining and sentiment analysis to the tweets with hashtags and keywords related to the migration and to the COVID-19 pandemic. Results show that tweets related to migration express a sense of emergency and also invasion. No major changes occurred in the period of the pandemic in comparison with the previous period. Indeed, both negative and positive sentiments are present in the tweets in both periods, confirming a certain polarization in the public discourse about migration

Exploring the German-Language Twittersphere: Network Analysis of Discussions on the Syrian and Ukrainian Refugee Crises

This study conducts a comparative analysis of Twitter communication networks relating to the Syrian and Ukrainian refugee crises. Employing a network analysis approach, the study uses approximately 660,000 tweets to gain insights into the online discussion communities surrounding these crises. Tweets specifically discussing Syrian refugees were collected between 2015 and 2023, while those about Ukrainians were harvested from 2022 to 2023, utilizing the full-archive search endpoint of the Twitter API. By transforming retweets into communication networks between users, the study investigates the community structure within these networks. The findings reveal that the online anti-refugee community is smaller in size, more active, highly interconnected, and transcends national boundaries, in contrast to the opposing communities. These results underscore the need for increased social media engagement of pro-refugee voices and improved moderation practices to foster a more inclusive virtual public sphere

AI for social good: social media mining of migration discourse

The number of international migrants has steadily increased over the years, and it has become one of the pressing issues in today’s globalized world. Our bibliometric review of around 400 articles on Scopus platform indicates an increased interest in migration-related research in recent times but the extant research is scattered at best. AI-based opinion mining research has predominantly noted negative sentiments across various social media platforms. Additionally, we note that prior studies have mostly considered social media data in the context of a particular event or a specific context. These studies offered a nuanced view of the societal opinions regarding that specific event, but this approach might miss the forest for the trees. Hence, this dissertation makes an attempt to go beyond simplistic opinion mining to identify various latent themes of migrant-related social media discourse. The first essay draws insights from the social psychology literature to investigate two facets of Twitter discourse, i.e., perceptions about migrants and behaviors toward migrants. We identified two prevailing perceptions (i.e., sympathy and antipathy) and two dominant behaviors (i.e., solidarity and animosity) of social media users toward migrants. Additionally, this essay has also fine-tuned the binary hate speech detection task, specifically in the context of migrants, by highlighting the granular differences between the perceptual and behavioral aspects of hate speech. The second essay investigates the journey of migrants or refugees from their home to the host country. We draw insights from Gennep's seminal book, i.e., Les Rites de Passage, to identify four phases of their journey: Arrival of Refugees, Temporal stay at Asylums, Rehabilitation, and Integration of Refugees into the host nation. We consider multimodal tweets for this essay. We find that our proposed theoretical framework was relevant for the 2022 Ukrainian refugee crisis – as a use-case. Our third essay points out that a limited sample of annotated data does not provide insights regarding the prevailing societal-level opinions. Hence, this essay employs unsupervised approaches on large-scale societal datasets to explore the prevailing societal-level sentiments on YouTube platform. Specifically, it probes whether negative comments about migrants get endorsed by other users. If yes, does it depend on who the migrants are – especially if they are cultural others? To address these questions, we consider two datasets: YouTube comments before the 2022 Ukrainian refugee crisis, and during the crisis. Second dataset confirms the Cultural Us hypothesis, and our findings are inconclusive for the first dataset. Our final or fourth essay probes social integration of migrants. The first part of this essay probed the unheard and faint voices of migrants to understand their struggle to settle down in the host economy. The second part of this chapter explored the viability of social media platforms as a viable alternative to expensive commercial job portals for vulnerable migrants. Finally, in our concluding chapter, we elucidated the potential of explainable AI, and briefly pointed out the inherent biases of transformer-based models in the context of migrant-related discourse. To sum up, the importance of migration was recognized as one of the essential topics in the United Nation’s Sustainable Development Goals (SDGs). Thus, this dissertation has attempted to make an incremental contribution to the AI for Social Good discourse

Fake News on Twitter related to the Refugee Crisis 2016 : An exploratory case study

Master's thesis in Information systems (IS501)Fake news has,inrecentyears,gained traction in the public media and as a research topic. Events such as the U.S 2016 presidential election, Brexit,the COVID-19 pandemic, amongst others,have seen tracesof large amounts offake news in social media. Social media sites like Twitter have enabled individuals, politicians,and companies to sharecontent and opinions witha large numberof peopleacross the globe. This opportunityfor mass communication has also ledtoTwitter becoming a place for fake news sharing. Various narratives by various actors partakein the same public discussions,andknowing whatis true and whatis fake is increasingly difficult. The purpose of this study wastoexamine and analyze a previously not studied dataset of 14.3 million tweets related to the 2016 refugee crisisand attemptto find traces of fake news. Theresearch approachchosenwas an exploratory case studywith mixed data analysis.The analyzed focusedon findingthe characteristicsof tweets, the most prominent topics,identifyingfake news,some of the actors(webpages) spreading fake news,and classify the type of fake news.To identify what content was fake, an extensive amount of literature in combination with three fact-checking services were utilized