Relocating Home: Second-Generation East African Women\u27s Twitter-Use as sites of Homeplace, Identity, and Memory

Homeplace, a concept credited to bell hooks (1991), was conceptualized through the practice and resiliency of Black women as they historically transformed the home as a space for reclamation of resistance and freedom. Through digital and social technologies, home is capable of manifesting outside of heteronormative meanings regarding spatiality and assumed gendered roles. This study explores second-generation East African women’s utilization of Twitter as a diasporic tool for homeplace, identity, and memory. This research incorporated a qualitative-phenomenological approach by interviewing ten participants from the ages of 18-26 in the United States, followed by a textual analysis of Twitter. Through coding cycles of transcriptions, key hashtags and phrases were pulled from participant’s interviews to guide the textual analysis. This research explores ways digital spaces are utilized and whether they provide adequate, fulfilling, and freeing manifestations of identity and home that are not often permitted to African diasporic communities in their realities

Metadata Matters: Adaptation Methods For Robust Document Classification

Metadata, implicitly embedded in documents such as time, demographic factors and user interests, can cause language variations and impact performance of document classifiers. For example, language shifts over periods of time, and males and females express sentiment differently. However, models for document classification, the automatic categorization of documents into categories, typically ignore document metadata. In this thesis, we focus on two types of document metadata, temporality and user factors. We propose to use domain adaptation by treating each metadata attribute as domains (e.g., gender domains: male vs. female), aiming to integrate temporality and user factors into document classifiers and improve classification performance. First, we propose temporality adaptation that explicitly incorporates time into the representation learning process via feature augmentation and diachronic word embedding. The feature augmentation method aims to learn time-independent feature weights for document classifiers. We then develop an end-to-end time-adapted model with the diachronic word embedding under a time-driven framework. Second, we propose user factor adaptation that models demographic attributes and user interests using multitask learning. To model demographic attributes, document classifiers jointly predict demographic factors and document categories. We further develop a multitask user embedding that jointly learns language, user behaviors and user interests. We examine and visualize impacts of temporality and user factor on word, topic, semantic and classifier levels. Benefits of adapting demographic attributes motivate us to examine if domain adaptation can reduce demographic biases. We release a multilingual hate speech corpus with author-level demographic labels. We examine demographic variations of user language and demographic biases of document classifiers. Following this, to reduce demographic bias, we apply a feature augmentation method to learn demographic-independent classifiers.</p

Quantitative intersectional data (QUINTA): a #metoo case study

This research began as an investigation of the #metoo movement, with the initial impetus to illuminate the voices located on the margins, those who often go unheard or are never recognized. This work aimed to understand the intersectional aspects of how these hashtag variations of the hashtag #metoo (i.e. #metoomosque, #churchtoo, #metoodisable, #metooqueer, #metoochina, etc) reveal the inequities of the #metoo movement on Twitter. The proliferation of these hashtag variations has often been ignored by scholars, and therefore absorbed into the larger #metoo movement conversation on Twitter. Therefore, the term `hashtag derivative\u27 was created to describe the variation on the theme of its original hashtag, strongly reflecting its composition. Moreover, a critical theory such as Intersectionality is well-equipped to explore how overlapping identities encounter structure social reality relationship to power. Amid a pandemic and racial unrest, the true capabilities of Intersectionality to describe inequities and injustices beyond the singular social position of race and gender are not widely understood. Data science, is not absolved of its role in inequities and injustices merely by dint of being a quantitative field that claims to ``objectivity\u27\u27. Social scientists have illuminated the racism, sexism, ableism, transphobia, homophobia, prejudice, bigotry, and bias embedded in data science\u27s technology, tools, and algorithms. This has, direct and indirectly, grave consequences on an entire community as a whole as well as marginalized communities. The application of Intersectionality into a quantitative field can provide researchers a formal structure to be more conscientious about how to critique, develop, and design their data science processes, while also reckoning with their own positioning in relationship to the data. In this way, Intersectionality is inclusive in terms of data equity yet adds an additional layer of accountability to the researcher. This research leads to the three critical contributions of this work: (1) creating a more concise terminology to describe the phenomenon of hashtag variation, known as hashtag derivatives, (2) defining the historical context of Intersectionality and building a formal case for this to be properly contextualized in the Computer Science field (in particular Data Science), and (3) developing the Quantitative Intersectional Data (QUINTA) Framework which data scientists and scholars can use to be more equitable, inclusive and accountable for their role in the data science process

Sustainability in the Global-Knowledge Economy

Knowledge affects all aspects of the economy, but digitalization probably represents the most ubiquitous of its appearances. This book analyzes, from a constructive point of view, some of its applications, extracting lessons to maximize its utility and exporting its use to other sectors. It also shows the caveats of its applications, allowing managers to learn its difficulties and how to overcome them from real-life cases. All the information is presented in an academic and rigorous way and represents an excellent starting point to study the effects of digitalization for both practitioners and researchers

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

Combating Misinformation on Social Media by Exploiting Post and User-level Information

Misinformation on social media has far-reaching negative impact on the public and society. Given the large number of real-time posts on social media, traditional manual-based methods of misinformation detection are not viable. Therefore, computational approaches (i.e., data-driven) have been proposed to combat online misinformation. Previous work on computational misinformation analysis has mainly focused on employing natural language processing (NLP) techniques to develop misinformation detection systems at the post level (e.g., using text and propagation network). However, it is also important to exploit information at the user level in social media, as users play a significant role (e.g., post, diffuse, refute, etc.) in spreading misinformation. The main aim of this thesis is to: (i) develop novel methods for analysing the behaviour of users who are likely to share or refute misinformation in social media; and (ii) predict and characterise unreliable stories with high popularity in social media. To this end, we first highlight the limitations in the evaluation protocol in popular rumour detection benchmarks on the post level and propose to evaluate such systems using chronological splits (i.e., considering temporal concept drift). On the user level, we introduce two novel tasks on (i) early detecting Twitter users that are likely to share misinformation before they actually do it; and (ii) identifying and characterising active citizens who refute misinformation in social media. Finally, we develop a new dataset to enable the study on predicting the future popularity (e.g. number of likes, replies, retweets) of false rumour on Weibo

Researching Non-state Actors in International Security

This volume provides researchers and students with a discussion of a broad range of methods and their practical application to the study of non-state actors in international security. All researchers face the same challenge, not only must they identify a suitable method for analysing their research question, they must also apply it. This volume prepares students and scholars for the key challenges they confront when using social-science methods in their own research. To bridge the gap between knowing methods and actually employing them, the book not only introduces a broad range of interpretive and explanatory methods, it also discusses their practical application. Contributors reflect on how they have used methods, or combinations of methods, such as narrative analysis, interviews, qualitative comparative analysis (QCA), case studies, experiments or participant observation in their own research on non-state actors in international security. Moreover, experts on the relevant methods discuss these applications as well as the merits and limitations of the various methods in use. Research on non-state actors in international security provides ample challenges and opportunities to probe different methodological approaches. It is thus particularly instructive for students and scholars seeking insights on how to best use particular methods for their research projects in International Relations (IR), security studies and neighbouring disciplines. It also offers an innovative laboratory for developing new research techniques and engaging in unconventional combinations of methods. This book will be of much interest to students of non-state security actors such as private military and security companies, research methods, security studies and International Relations in general

On the Promotion of the Social Web Intelligence

Given the ever-growing information generated through various online social outlets, analytical research on social media has intensified in the past few years from all walks of life. In particular, works on social Web intelligence foster and benefit from the wisdom of the crowds and attempt to derive actionable information from such data. In the form of collective intelligence, crowds gather together and contribute to solving problems that may be difficult or impossible to solve by individuals and single computers. In addition, the consumer insight revealed from social footprints can be leveraged to build powerful business intelligence tools, enabling efficient and effective decision-making processes. This dissertation is broadly concerned with the intelligence that can emerge from the social Web platforms. In particular, the two phenomena of social privacy and online persuasion are identified as the two pillars of the social Web intelligence, studying which is essential in the promotion and advancement of both collective and business intelligence. The first part of the dissertation is focused on the phenomenon of social privacy. This work is mainly motivated by the privacy dichotomy problem. Users often face difficulties specifying privacy policies that are consistent with their actual privacy concerns and attitudes. As such, before making use of social data, it is imperative to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, we utilize user social footprints to detect their privacy preferences automatically. An unsupervised collaborative filtering approach is proposed to characterize the attributes of publicly available accounts that are intended to be private. Unlike the majority of earlier studies, a variety of social data types is taken into account, including the social context, the published content, as well as the profile attributes of users. Our approach can provide support in making an informed decision whether to exploit one\u27s publicly available data to draw intelligence. With the aim of gaining insight into the strategies behind online persuasion, the second part of the dissertation studies written comments in online deliberations. Specifically, we explore different dimensions of the language, the temporal aspects of the communication, as well as the attributes of the participating users to understand what makes people change their beliefs. In addition, we investigate the factors that are perceived to be the reasons behind persuasion by the users. We link our findings to traditional persuasion research, hoping to uncover when and how they apply to online persuasion. A set of rhetorical relations is known to be of importance in persuasive discourse. We further study the automatic identification and disambiguation of such rhetorical relations, aiming to take a step closer towards automatic analysis of online persuasion. Finally, a small proof of concept tool is presented, showing the value of our persuasion and rhetoric studies