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    PublicationOpen Access
    A task set proposal for automatic protest information collection across multiple countries
    (Springer, 2019) Department of Sociology; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Yoltar, Çağrı; Yüret, Deniz; Gürel, Burak; Duruşan, Fırat; Mutlu, Osman; Teaching Faculty; Faculty Member; Researcher; Faculty Member; Faculty Member; Researcher; Department of Sociology; Department of Computer Engineering; Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; N/A; 28982; N/A; 179996; 219277; N/A; N/A
    We propose a coherent set of tasks for protest information collection in the context of generalizable natural language processing. The tasks are news article classification, event sentence detection, and event extraction. Having tools for collecting event information from data produced in multiple countries enables comparative sociology and politics studies. We have annotated news articles in English from a source and a target country in order to be able to measure the performance of the tools developed using data from one country on data from a different country. Our preliminary experiments have shown that the performance of the tools developed using English texts from India drops to a level that are not usable when they are applied on English texts from China. We think our setting addresses the challenge of building generalizable NLP tools that perform well independent of the source of the text and will accelerate progress in line of developing generalizable NLP systems.
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    PublicationOpen Access
    Challenges and applications of automated extraction of socio-political events from text (CASE 2021): workshop and shared task report
    (Association for Computational Linguistics (ACL), 2021) Tanev, Hristo; Zavarella, Vanni; Piskorski, Jakub; Yeniterzi, Reyyan; Villavicencio, Aline; Department of Sociology; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Mutlu, Osman; Yüret, Deniz; Teaching Faculty; Faculty Member; Researcher; Faculty Member; Department of Sociology; Department of Computer Engineering; College of Social Sciences and Humanities; Graduate School of Sciences and Engineering; College of Engineering; N/A; 28982; N/A; 179996
    This workshop is the fourth issue of a series of workshops on automatic extraction of sociopolitical events from news, organized by the Emerging Market Welfare Project, with the support of the Joint Research Centre of the European Commission and with contributions from many other prominent scholars in this field. The purpose of this series of workshops is to foster research and development of reliable, valid, robust, and practical solutions for automatically detecting descriptions of sociopolitical events, such as protests, riots, wars and armed conflicts, in text streams. This year workshop contributors make use of the state-of-the-art NLP technologies, such as Deep Learning, Word Embeddings and Transformers and cover a wide range of topics from text classification to news bias detection. Around 40 teams have registered and 15 teams contributed to three tasks that are i) multilingual protest news detection, ii) fine-grained classification of socio-political events, and iii) discovering Black Lives Matter protest events. The workshop also highlights two keynote and four invited talks about various aspects of creating event data sets and multi- and cross-lingual machine learning in few- and zero-shot settings.
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    PublicationOpen Access
    Çin’in yükselişi ve yeni kapitalizm
    (Sosyoekonomi Society, 2018) Department of Sociology; Yörük, Erdem; Faculty Member; Department of Sociology; College of Social Sciences and Humanities; 28982
    This article presents a theoretical discussion about the new forms of capitalism in the context of the economic and political rise of China. The article raises a discussion on the changes that the rise of China has instigated in both China and the world capitalism. This is considered in the context of mode of production, international trade, state and capital, by analysing China and capitalism from the perspective of long historical periods. In doing this, the article benefits from the work of and polemics between Giovanni Arrighi, Joel Andreas ve Richard Walker, who provided very important contemporary debates on this issue in the field of historical sociology.
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    PublicationOpen Access
    Contentious welfare: the Kurdish conflict and social policy as counterinsurgency in Turkey
    (Wiley, 2020) Department of Sociology; Yörük, Erdem; Yoltar, Çağrı; Faculty Member; Researcher; Department of Sociology; College of Social Sciences and Humanities; 28982; N/A
    The period since the 1990s has witnessed the expanding political influence of the Kurdish movement across the country as well as a transformation in the welfare system, manifesting itself mainly in the emergence of extensive social assistance programs. While Turkish social assistance policy has been formally neutral regarding who is entitled to state aid, Kurds have been de facto singled out by these new welfare programs, as is shown by existing quantitative work. Based on a discourse analysis of legislation, parliamentary proceedings, and news media, this article examines the ways in which Turkish governments and policymakers consider the Kurdish question in designing welfare policies. We illustrate that Kurdish mobilization has become a central theme that informed the transformation of Turkish welfare system over the past three decades.
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    PublicationOpen Access
    Cross-context news corpus for protest event-related knowledge base construction
    (Massachusetts Institute of Technology (MIT) Press, 2021) Department of Sociology; N/A; Department of Computer Engineering; Yörük, Erdem; Hürriyetoğlu, Ali; Gürel, Burak; Duruşan, Fırat; Yoltar, Çağrı; Mutlu, Osman; Yüret, Deniz; Faculty Member; Teaching Faculty; Faculty Member; Researcher; Researcher; Faculty Member; Department of Sociology; Department of Computer Engineering; College of Social Sciences and Humanities; Graduate School of Sciences and Engineering; College of Engineering; 28982; N/A; 219277; N/A; N/A; N/A; 179996
    We describe a gold standard corpus of protest events that comprise various local and international English language sources from various countries. The corpus contains document-, sentence-, and token-level annotations. This corpus facilitates creating machine learning models that automatically classify news articles and extract protest event-related information, constructing knowledge bases that enable comparative social and political science studies. For each news source, the annotation starts with random samples of news articles and continues with samples drawn using active learning. Each batch of samples is annotated by two social and political scientists, adjudicated by an annotation supervisor, and improved by identifying annotation errors semi-automatically. We found that the corpus possesses the variety and quality that are necessary to develop and benchmark text classification and event extraction systems in a cross-context setting, contributing to the generalizability and robustness of automated text processing systems. This corpus and the reported results will establish a common foundation in automated protest event collection studies, which is currently lacking in the literature.
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    PublicationOpen Access
    Discovering Black Lives Matter events in the United States: Shared Task 3, CASE 2021
    (Association for Computational Linguistics (ACL), 2021) Giorgi, Salvatore; Zavarella, Vanni; Tanev, Hristo; Stefanovitch, Nicolas; Hwang, Sy; Hettiarachchi, Hansi; Ranasinghe, Tharindu; Kalyan, Vivek; Tan, Paul; Tan, Shaun; Andrews, Martin; Hu, Tiancheng; Stoehr, Niklas; Re, Francesco Ignazio; Vegh, Daniel; Atzenhofer, Dennis; Curtis, Brenda; Department of Sociology; Hürriyetoğlu, Ali; Teaching Faculty; Department of Sociology; College of Social Sciences and Humanities
    Evaluating the state-of-the-art event detection systems on determining spatio-temporal distribution of the events on the ground is performed unfrequently. But, the ability to both (1) extract events ""in the wild"" from text and (2) properly evaluate event detection systems has potential to support a wide variety of tasks such as monitoring the activity of socio-political movements, examining media coverage and public support of these movements, and informing policy decisions. Therefore, we study performance of the best event detection systems on detecting Black Lives Matter (BLM) events from tweets and news articles. The murder of George Floyd, an unarmed Black man, at the hands of police officers received global attention throughout the second half of 2020. Protests against police violence emerged worldwide and the BLM movement, which was once mostly regulated to the United States, was now seeing activity globally. This shared task asks participants to identify BLM related events from large unstructured data sources, using systems pretrained to extract socio-political events from text. We evaluate several metrics, assessing each system's ability to evolution of protest events both temporally and spatially. Results show that identifying daily protest counts is an easier task than classifying spatial and temporal protest trends simultaneously, with maximum performance of 0.745 (Spearman) and 0.210 (Pearson r), respectively. Additionally, all baselines and participant systems suffered from low recall (max.5.08), confirming the high impact of media sourcing in the modelling of protest movements.
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    PublicationOpen Access
    Family role in in-patient rehabilitation: the cases of England and Turkey
    (Taylor _ Francis, 2019) Shakespeare, Tom; Yardımcı, Sibel; Department of Sociology; Bezmez, Dikmen; Faculty Member; Department of Sociology; College of Social Sciences and Humanities; 101788
    Purpose: this article explores the differences between experiences of family role in in-patient rehabilitation in Turkey and England. Background: the literature predominantly assumes family presence in rehabilitation as positive, because it draws upon Western cases, where care is delivered fully by professionals, and patients may feel isolated during hospital stays. Analyses of other contexts provide a more nuanced view. Method: this qualitative research included in-depth interviews (Turkey: 42, England: 18) with people with disabilities (n = 39), their families (n = 8) and hospital staff (n = 13); hospital ethnography (Turkey), focus groups (England: 3 groups involving 4 doctors, 5 nurses, 6 therapists), and participant-observation (England: 5 families). Thematic analysis highlights experiences of family involvement across different contexts. Results: Families are differently integrated in rehabilitation in England and Turkey. In England, where family presence is regulated and relatively limited, people with disabilities feel more isolated and see family as a major form of support. In Turkey, where family presence is unregulated and intense, they enjoy family as an agent of intra-hospital socialising, but find it disabling when it implies a loss of privacy and individuality. Conclusion: family involvement in rehabilitation should support social interaction but allow people with disabilities to remain independent. Implications for rehabilitation Family involvement in rehabilitation can be both enabling and disabling. Existing literature draws upon rehabilitation practices, where family presence is limited and perceived as positive. An analysis of cases, where families are integral to the health care system (e.g., Turkey), can provide a nuanced view of family integration, which can be both enabling and disabling. Rehabilitation processes and health professionals need to integrate families in ways that will enrich social interaction, but still allow people with disabilities to retain their independence.
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    PublicationOpen Access
    Indigenous unrest and the contentious politics of social assistance in Mexico
    (Elsevier, 2019) Öker, İbrahim; Şarlak, Lara; Department of Sociology; Yörük, Erdem; Faculty Member; Department of Sociology; College of Social Sciences and Humanities; 28982
    Is social assistance being used to contain ethnic and racial unrest in developing countries? There is agrowing literature on social assistance policies in the Global South, but this literature largely focuseson economic and demographic factors, underestimating the importance of contentious politics. The caseof Mexico shows that social assistance programs are disproportionately directed to indigenous popula-tions, leading to diminished protest participation. Drawing on data from the 2010, 2012 and 2014 roundsof the Latin American Public Opinion Project, we apply multivariate regression analysis to examine thedeterminants of social assistance program participation in Mexico. Our study finds that after controllingfor income, household size, age, education, and employment status, indigenous ethnic identity is a keydeterminant in who benefits from social assistance in Mexico. Our results show that high ethnic disparityin social assistance is not only due to higher poverty rates among the indigenous population. Rather,indigenous people receive more social assistance mainly because of their ethnic identity. In addition, thisstudy demonstrates that indigenous people who benefit from social assistance programs are less likely tojoin anti-government protests. We argue that this ethnic targeting in social assistance is a result of thefact that indigenous unrest has become a political threat for Mexican governments since the 1990s.These results yield substantive support in arguing that the Mexican government uses social assistanceto contain indigenous unrest. The existing literature, which is dominated by structuralist explanations,needs to strongly consider the contentious political drivers of social assistance provision in the GlobalSouth for a full grasp of the phenomenon. Social assistance in Mexico is driven by social unrest and thissuggests that similar ethnic, racial, religious and contentious political factors should be examined in otherdeveloping countries to understand social assistance provisions.
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    PublicationOpen Access
    Multilingual protest news detection - shared task 1, CASE 2021
    (Association for Computational Linguistics (ACL), 2021) Liza, Farhana Ferdousi; Kumar, Ritesh; Ratan, Shyam; Department of Sociology; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Mutlu, Osman; Teaching Faculty; Faculty Member; Researcher; Department of Sociology; Department of Computer Engineering; College of Social Sciences and Humanities; Graduate School of Sciences and Engineering; N/A; 28982; N/A
    Benchmarking state-of-the-art text classification and information extraction systems in multilingual, cross-lingual, few-shot, and zero-shot settings for socio-political event information collection is achieved in the scope of the shared task Socio-political and Crisis Events Detection at the workshop CASE @ ACL-IJCNLP 2021. Socio-political event data is utilized for national and international policy- and decision-making. Therefore, the reliability and validity of such datasets are of utmost importance. We split the shared task into three parts to address the three aspects of data collection (Task 1), fine-grained semantic classification (Task 2), and evaluation (Task 3). Task 1, which is the focus of this report, is on multilingual protest news detection and comprises four subtasks that are document classification (subtask 1), sentence classification (subtask 2), event sentence coreference identification (sub-task 3), and event extraction (subtask 4). All subtasks have English, Portuguese, and Spanish for both training and evaluation data. Data in Hindi language is available only for the evaluation of subtask 1. The majority of the submissions, which are 238 in total, are created using multi- and cross-lingual approaches. Best scores are between 77.27 and 84.55 F1-macro for subtask 1, between 85.32 and 88.61 F1-macro for subtask 2, between 84.23 and 93.03 CoNLL 2012 average score for subtask 3, and between 66.20 and 78.11 F1-macro for subtask 4 in all evaluation settings. The performance of the best system for subtask 4 is above 66.20 F1 for all available languages. Although there is still a significant room for improvement in cross-lingual and zero-shot settings, the best submissions for each evaluation scenario yield remarkable results. Monolingual models outperformed the multilingual models in a few evaluation scenarios, in which there is relatively much training data.
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    PublicationOpen Access
    Overview of CLEF 2019 lab protestnews: extracting protests from news in a cross-context setting
    (Springer, 2019) Department of Sociology; Department of Computer Engineering; Hürriyetoğlu, Ali; Yörük, Erdem; Yüret, Deniz; Yoltar, Çağrı; Gürel, Burak; Mutlu, Osman; Akdemir, Arda; Teaching Faculty; Faculty Member; Faculty Member; Researcher; Faculty Member; Researcher; Department of Sociology; Department of Computer Engineering; Graduate School of Social Sciences and Humanities; Graduate School of Sciences and Engineering; N/A; 28982; 179996; N/A; 219277; N/A; N/A
    We present an overview of the CLEF-2019 Lab ProtestNews on Extracting Protests from News in the context of generalizable natural language processing. The lab consists of document, sentence, and token level information classification and extraction tasks that were referred as task 1, task 2, and task 3 respectively in the scope of this lab. The tasks required the participants to identify protest relevant information from English local news at one or more aforementioned levels in a cross-context setting, which is cross-country in the scope of this lab. The training and development data were collected from India and test data was collected from India and China. The lab attracted 58 teams to participate in the lab. 12 and 9 of these teams submitted results and working notes respectively. We have observed neural networks yield the best results and the performance drops significantly for majority of the submissions in the cross-country setting, which is China.