"This report sets out data on the extent of publisher adoption alongside motivations for joining TikTok; pulls together top tips from TikTok creators and discuss the metrics most commonly used to evaluate success; explores different strategies for engaging users on the platform, highlighting case st
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udies from early pioneers as well as independent news creators and activists; and looks at future opportunities for monetisation and ways in which publishers would like TikTok to better support reliable and trusted news sources." (Introduction and Key Findings, page 4)
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"What constitutes a data practice and how do contemporary digital media technologies reconfigure our understanding of practices in general? Autonomously acting media, distributed digital infrastructures, and sensor-based media environments challenge the conditions of accounting for data practices bo
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th theoretically and empirically. Which forms of cooperation are constituted in and by data practices? And how are human and nonhuman agencies distributed and interrelated in data-saturated environments? The volume collects theoretical, empirical, and historiographical contributions from a range of international scholars to shed light on the current shift from media to data practices." (Publisher description)
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"DataFest Africa is an annual event that seeks to celebrate data use in the region by bringing together a variety of stakeholders of diverse backgrounds such as government, civil society, academics, students and private industry experts under one roof and theme. Since 2019, Pollicy, with support fro
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m partners, has been organising DataFest, an annual celebration of data that has so far transformed from a Kampala edition (DataFest Kampala) to an African regional edition (DataFest Africa). DataFest Africa continues to bring together data enthusiasts, Civil Society Organisations, Government, private sector and more to discuss trends on data across the continent. The biggest so far, the 3rd edition of DataFest (now DataFest Africa) held under the theme: "Data Futures: Big Data, Little Data, and Everything in Between'' was a hybrid event that attracted a wide range of participants and stakeholders. The month-long edition started on June 10th to July 17th. We welcomed a total of 716 participants from 3 countries, a steady increase in our community, which has more than tripled since our first ever edition in 2019. This report is a reflection of key outcomes, numbers and impact of the 2022 edition of DataFest Africa." (Introduction)
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"This study employed three machine learning algorithms, Naïve Bayes, SVM, and a Balanced Random Forest to build a sentiment model that can detect Muslim sentiment about Muslim clerics’ anti-misinformation campaign on YouTube. Overall, 9701 comments were collected. An LDA-based topic model was als
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o employed to understand the most expressed topics in the YouTube comments. Results: The confusion matrix and accuracy score assessment revealed that the balanced random forest-based model demonstrated the best performance. Overall, the sentiment analysis discovered that 74 percent of the comments were negative, and 26 percent were positive. An LDA-based topic model also revealed the eight most discussed topics associated with ten keywords in those YouTube comments. Practical implications: The sentiment and topic model from this study will particularly help public health professionals and researchers to better understand the nature of vaccine misinformation and hesitancy in the Muslim communities." (Abstract)
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"This publication is the last of four reports from a regional study completed in 2021 and funded by the technical assistance of the Asian Development Bank (ADB) on Policy Advice for COVID-19 Economic Recovery in Southeast Asia. The project supports the recovery efforts of Southeast Asian countries t
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o return to their economic performance before the coronavirus disease (COVID-19) pandemic. It also assists countries in preparing for national, regional, or global transformations that may take place post-COVID-19. The focus countries are Cambodia, Indonesia, Myanmar, the Philippines, and Thailand, which tapped ADB's COVID-19 Pandemic Recovery Option facility. The study produced four reports on the following thematic areas: 1. Supporting post-COVID-19 economic recovery in Southeast Asia. 2. Strengthening domestic resource mobilization in Southeast Asia. 3. Implementing a green recovery in Southeast Asia. 4. Harnessing the potential of big data in post-pandemic Southeast Asia." (Foreword)
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"This Open Access book examines the ambivalences of data power. Firstly, the ambivalences between global infrastructures and local invisibilities challenge the grand narrative of the ephemeral nature of a global data infrastructure. They make visible local working and living conditions, and the reso
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urces and arrangements required to operate and run them. Secondly, the book examines ambivalences between the state and data justice. It considers data justice in relation to state surveillance and data capitalism, and reflects on the ambivalences between an “entrepreneurial state” and a “welfare state”. Thirdly, the authors discuss ambivalences of everyday practices and collective action, in which civil society groups, communities, and movements try to position the interests of people against the “big players” in the tech industry." (Publisher description)
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"Argues that what makes AI socially relevant and useful is not intelligence at all but something even more human: communication. If machines are going to improve their ability to address ever more important human issues, it will not be because they have learned to think like people, but because we h
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ave learned to communicate with them." (Publisher description)
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"Access Rules mounts a strong and hopeful argument for how informational tools at present in the hands of a few could instead become empowering machines for everyone. By forcing data-hoarding companies to open access to their data, we can reinvigorate both our economy and our society. Authors Viktor
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Mayer-Schönberger and Thomas Ramge contend that if we disrupt monopoly power and create a level playing field, digital innovations can emerge to benefit us all. Over the last twenty years, Big Tech has managed to centralize the most relevant data on their servers, and data has become the most important raw material for innovation. Dominant oligopolists like Facebook, Amazon, and Google, contrary to their reputation as digital pioneers, are in fact slowing down innovation and progress for the benefit of their shareholders--and at the expense of customers, the economy, and society. As Access Rules compellingly argues, ultimately it is up to us to force information giants, wherever they are located, to share their treasure troves of data with others. In order for us to limit global warming, contain a virus like COVID-19, or successfully fight poverty, everyone must have access to data - citizens and scientists, start-ups and established companies, as well as the public sector and NGOs. When everyone has access to the informational riches of the data age, the nature of digital power will change. Information technology will find its way back to its original purpose: empowering all of us to use information so we can thrive as individuals and as societies." (Publisher description)
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"Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also
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the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to the need for appropriate tools and methods which can be used to break the algorithmic power." (Publisher description)
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"Diese Publikation ist im Rahmen des Projekts AutoCheck – Handlungsanleitung für den Umgang mit automatisierten Entscheidungssystemen für Antidiskriminierungsstellen entstanden. Sie ist eine erste Einführung in die Diskriminierungsrisiken beim Einsatz von automatisierten Entscheidungssystemen.
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Für ein tiefergehendes Verständnis der Thematik werden im Rahmen des Projekts Weiterbildungskonzepte entwickelt, die als Workshops für Multiplikator*innen veröffentlicht und angeboten werden. Wir wenden uns mit dieser Handreichung in erster Linie an Mitarbeitende von Antidiskriminierungsstellen in Deutschland. Durch Kompetenzaufbau in diesem zunehmend bedeutsamen Themenfeld sollen sie in die Lage versetzt werden, Risiken besser zu erkennen, einzuschätzen und dadurch in konkreten Diskriminierungsfällen Betroffene besser unterstützen zu können. Darüber hinaus soll diese Publikation auch den von Diskriminierung Betroffenen die Möglichkeit geben, sich über das Thema zu informieren. Der Fokus liegt auf dem Zugang zu Gütern und privaten Dienstleistungen, beispielsweise Benachteiligungen im Online-Handel oder beim Abschließen einer Versicherung. Nur am Rande gehen wir auf andere Anwendungsbereiche des Allgemeinen Gleichbehandlungsgesetzes (AGG) ein." (Einleitung)
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"The role of algorithms for producing and curating content as well as potential outcomes of these mechanisms is one of the most debated issues in existing communication research. “Communicating algorithms” affect processes of political, social and interpersonal communication. A broad variety of
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communication fields is thus currently touched on by algorithms, ranging from news exposure, public opinion forming, information retrieval, and political communication processes among others. However, a scientific sound and objective consideration of algorithms as actors in digital (mass) communication is still scarce. The special issue “Algorithms and Communication” addresses this research gap. It presents theoretical as well as empirical results in important fields of communication science, such as media literacy, news aggregation or robotics. With this, it aims to shed light on the black-box of algorithms as “hidden actors” in communication processes." (Abstract)
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"Intended as a guide for policy-makers and other stakeholders in crafting a national AI and data strategy for development, the report highlights opportunities and outlines good policy and regulatory practices for implementation, while also flagging key challenges and offering hands-on suggestions in
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managing and overcoming these roadblocks. The report describes the main building-blocks of a national AI and data system for development, including governance, regulation, ethical considerations, digital and data skills, the overall digital environment, the technological innovation landscape and opportunities for international collaboration. It goes on to detail the main components of an effective AI and data system action plan, including the principles governing stakeholder engagement, the setting of clear milestones and budgets and administrative structures to support implementation and coordination mechanisms." (Foreword)
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"This report aims to answer two fundamental questions. First, how can data better advance development objectives? Second, what kind of data governance arrangements are needed to support the generation and use of data in a safe, ethical, and secure way while also delivering value equitably? One impor
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tant message of this report is that simply gathering more data is not the answer. Significant data shortfalls, particularly in poor countries, do exist, but the aim of this report is to shift the focus toward using data more effectively to improve development outcomes, particularly for poor people in poor countries." (Overview, page 3)
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"Seitdem das Fernsehen Politik macht, werden Einwände und Kritik gegen Regierende über den Bildschirm kommuniziert – die Bürger*innen sind in passives Zuschauen gedrängt. Der Aufstieg der sozialen Medien dagegen verspricht neue Möglichkeiten der Teilhabe. Doch wird der öffentliche Raum immer
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undurchsichtiger, komplexer und schwerer zu fassen: Meinungen und Verhaltensmuster werden zunehmend durch Algorithmen kontrolliert, die globalen Unternehmen unterstehen. Welche Alternativen bleiben angesichts dieser Enteignung? Dissidenz und Hacking? Im Spiegel der forcierten (Zwangs-)Digitalisierung durch die Covid-19-Pandemie widmet sich Néstor García Canclini aus kultur- und politikwissenschaftlicher Perspektive diesem Komplex." (Verlagsbeschreibung)
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"Just as in the global context, new tools are being used by the media in Latin America and Central and Eastern Europe (CEE). The use of artificial intelligence and machine learning (ALI/ML) is not limited to large, corporate media. But the reality of different news organisations with a variety of bu
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dgets and markets can vary deeply. In Latin America, only a handful of media organisations are embracing AI or machine learning in house, most notably in Argentina, Perú and México and none as part of a long term effort to embrace the technology. While most of the news organisations consulted are using some sort of AI implementation through vendors or third party solutions and there is strong appetite for more it is rarely part of a strategic vision. In CEE, digital natives are embracing AI/ML solutions and the region has been produced a few AI/ML based third party solution providers with global reach or ambitions. Competition for talent is a major bottleneck, as media have to compete with the global outsourcing of IT jobs to the region. The other challenge is state pressure on media, especially in such markets as Russia or Belarus, which makes long-term planning and investment impractical." (Key findings, page 5)
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"Big data can contribute to the evidence base in development sectors where evaluations are often infeasible due to data issues. Given the rapidly increasing availability of big data and improving computation capacity, there is a great potential for using big data in future impact evaluations. Big da
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ta can also contribute to evaluations through providing new ways to identify control groups and establish counterfactuals, and can strengthen the analysis with data on pre-programme trends, covariates, and sub-groups, as well as enabling better robustness analyses. However, there are several analytical, ethical, and logistical challenges that may hinder the use of big data in impact evaluations. Standards should be set for the reporting of data quality issues, data representativeness, and data transparency. More interaction is needed between big data analysts, remote sensing scientists, and evaluators." (Conclusion)
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