Reviewerly/exHarmony huggingface dataset:
Penerapan Metode Recurrent Neural Network dengan Pendekatan Long Short-Term Memory (LSTM) Untuk Prediksi Harga Saham

Investing in shares is now increasingly popular and growing rapidly in Indonesia. By investing in shares, investors will get quite fast and large profits in a fairly short time. Investors need to analyze previous stock movements as a form of investment strategy to get maximum investment results. Many techniques have been applied to predict stock prices, one of which uses techniques in Deep Learning such as Recurrent Neural Network. In this research, research was conducted on stock price predictions using the Recurrent Neural Network method with the Long-Short Term Memory (LSTM) approach on BBNI stock data. The research only uses close data or data about daily closing stock prices. In designing LSTM there are several things that are configured such as dropout size, density, activation function, and number of neurons used, and in the training process one of the optimizers provided by the Keras framework is used, namely the ADAM (Adaptive Moment Estimation) optimizer. The test scenario is carried out using a number of epochs of 10 and 20 with a batch size of 32. The test results will produce MAE and MAPE values, where the lower the MAE and MAPE values, the better the model's performance in making accurate predictions. The test results using epoch 10 got an MAE value of 0.0286 and a MAPE value of 0.0488, while the test results using epoch 20 got an MAE value of 0.0150 and a MAPE value of 0.0257.
Implementasi Aplikasi Pencatatan Data Magang Mahasiswa Berbasis Mobile Menggunakan Kodular Menggunakan Metode Waterfall

This research aims to develop a mobile application for recording student internship data by applying a software development approach using the Waterfall model. The Waterfall model, known for its linear and structured stages, was chosen to ensure that the development process was carried out in an organized and documented manner. The main objective of this research is to implement a mobile application using the Kodular platform with a focus on recording student internship data, with the hope of increasing efficiency, accuracy and monitoring of internship activities. This research adopts the Waterfall model through the stages of analysis, design, implementation, testing and maintenance. The analysis stage is carried out to understand the needs and objectives of the application in depth, while the design stage is focused on the required structure and functionality. The implementation phase involves transforming the design into an optimally functioning mobile application. Testing is carried out to ensure application stability and performance, while maintenance is carried out to respond to changing needs or technology updates. By integrating the Waterfall model, this research aims to provide a structured framework that ensures each development stage is carried out carefully. It is hoped that the results of this research can create an efficient mobile application for recording student internship data, so that it can make a positive contribution in increasing efficiency and monitoring internship activities as a whole. Apart from that, this research can also be a reference for future mobile application developers who want to adopt the Waterfall model for similar projects, taking into account special needs and characteristics in the context of recording student internship data.
Macroeconomic Factors and Economic Development: An Insight into China Economy Using ARDL Approach

It is difficult to identify the factors that drive the macroeconomic growth of developing countries because of the social, infrastructural, and governance factors involved. To verify the existence of this connection, this study examines the macroeconomic variables that have influenced China's economy from 1979 to 2020, using data acquired from the world development indicators. Through the use of the ARDL bound model test, it is shown that China's economy is positively impacted by factors such as capital formation, government spending, foreign direct investment (FDI), exchange rate, human capital development, and trade openness over both the long and the short term. On the other hand, inflation has a detrimental impact on economic growth both in the long run and in the short run. Consequently, the real GDP per capita should be increased, human capital should be strengthened, trade should be reformed, and macroeconomic circumstances should be stabilized under fiscal and monetary policy. By enhancing human capital, and foreign direct investment, reforming government expenditures, and lowering inflation, China has the potential to achieve sustainable economic development. This study offers scholars and policymakers a fresh econometric examination of the primary macroeconomic elements that are contributing to the economy.
Yellow Ground Squirrel Algorithm (YGSA): A Novel Metaheuristic Algorithm for Global Optimization

In recent years, numerous methods have emerged for optimizing problems inspired by nature. Each of these methods, according to their nature, has applications for solving specific problems with unique characteristics. Given the wide array of optimization problems across various fields, introducing novel algorithms with distinct behaviors can be both appealing and practical. In this article, a nature-inspired meta-heuristic optimization algorithm called Yellow Ground Squirrel Algorithm (YGSA) is introduced. The main idea behind YGSA is to simulate the natural behavior of a yellow ground squirrel in terms of pursuit and evasion from a farmer (the predator) and reaching the nest. The objective of the proposed algorithm is to enhance and attain a more equitable equilibrium between the fundamental stages of exploration and exploitation in contrast to preceding approaches. This balance arises from the fact that the yellow ground squirrel, during its escape, simultaneously tries to increase the distance from the farmer (the predator) and decrease the distance to its nest (burrow). To validate the proposed algorithm, 56 benchmark evaluation functions from various unimodal and multimodal types have been evaluated. The evaluation results are compared with the HBO, EBO-CMAR, FO, SCA, PSO, CS, GSA, MVO, and WSO algorithms. The optimization results for unimodal test functions demonstrate the high exploitation capability of YGSA in approaching the optimal solution, while the optimization results for multimodal test functions indicate the strong exploration ability of YGSA in finding the main optimal region in the search space.
EVERYDAY MISOGYNY: DISCOURSES ABOUT DEPP V HEARD ON TWITTER

This paper examines the manifestation of 'everyday' online misogyny. Social media platforms are often deeply unsafe spaces for women, particularly women who speak publicly about feminist issues. In response to a number of public controversies over the last decade, platforms have introduced a range of different design interventions and policy changes. However, these interventions have predominantly focused on the most extreme, unambiguous manifestations of online misogyny. Current literature on gender-based violence emphasises that ‘everyday’ expressions of misogyny play a significant role in normalising violence against women and reinforcing the beliefs that underpin the more exceptional misogynistic attacks. This paper presents the initial findings of a case study of everyday misogyny on Twitter in discourses about the $2 trial. It aims to contribute to a deeper understanding of how everyday misogyny manifests in ordinary language and debates on social media platforms, as a step towards developing better mechanisms for identifying and responding to online misogyny. Our preliminary findings challenge platforms' traditional reliance on counterspeech-based approaches to addressing the harms of everyday misogyny. Rather than serving as a remedy, this study suggests that online debate about women's experiences of violence can instead, in some circumstances, become a vehicle for oppression, a manifestation of everyday online misogyny.
CRUISING TIKTOK: USING ALGORITHMIC FOLK KNOWLEDGE TO EVADE CISHETERONORMATIVE CONTENT MODERATION

This paper examines crackdowns on queer content on TikTok and creative responses of content creators to circumvent biased content moderation and cisheteronormative censorship. The first portion of the paper demonstrates TikTok’s recurrent cisheteronormative biases in content moderation decisions and examines select instances of LGBTQ+ content that has been censored on the platform. It also works to situate this within a broader trend of LGBTQ+ censorship across internet platforms. The second portion of the paper examines how LGBTQ+ TikTok users have built up folk knowledges and intuitive understandings of TikTok’s blackboxed algorithms and opaque content moderation policies, situating this discussion within theories of the ‘algorithmic imaginary’. It catalogs the myriad ways that TikTok users work to circumvent LGBTQ+ censorship on the platform (e.g. by tactically obscuring key words in both speech and text and obscuring body parts and scenes). In the final portion of the paper, I draw on the concept of ‘cruising’ and other constitutive silences of LGBTQ+ existence to show how LGBTQ+ users are particularly well suited to producing folk knowledge about blackboxed algorithms. In closing, I examine the affordances and the limitations of LGBTQ+ users’ approach to navigating platform governance – and content moderation practices more specifically – as well as call for more organized and collective action in search of more permanent changes towards LGBTQ+-friendly platforms.
WEB HISTORIES IN THE MAKING: WEB ARCHIVES &amp; THE LOGICS OF PRACTICE

Historically-situated accounts of the Web have a long history within the field of internet studies. Drawing on diverse methodologies and forms of data, web histories of platforms, cultures and communities of practice have illuminated the rich, but often transient and shifting nature of life online. Many web histories rely upon researchers capturing, collecting, and generating their own data through time, though some have also engaged with web archives as a means for studying the past online. However, web archive data have never fulfilled the requirements of positivist ideals such as ‘representativeness’ or objectivity, and the methodological consequences of this observation currently do not go far enough. This panel aims to shift and reframe current discussions of the ‘promise’ of web archives for web historiography, towards identifying what underlying logics or ideals drive and motivate various actors engaged in this work. We argue that not only do the logics underpinning the practices of collecting and archiving the Web deserve further attention, but also the practices of internet researchers who aim to use these materials for studying the Web. Each paper contribution in this panel builds on web archive criticism by situating archived web material as fundamentally tied to the logics of practice. These underpinnings affect not only the formation of web archives, but also the methodological approaches researchers take. We therefore suggest new ways for conceptualising the ‘doing’ of web histories, tying them to an assemblage of people, practice and data that shape how we can come to understand the Web.
EXPLORING THE CONTEXTUAL COMPLEXITIES OF VIOLENCE ON DIGITAL PLATFORMS: INTERSECTIONS, IMPACTS, AND SOLUTIONS

As social media platforms face increasing complexity, severity, and pervasiveness of violent content, it is of utmost importance for researchers, educators, policymakers, and users alike to regard digital violence as an imminent threat. Indeed, violent content is widely acknowledged as an inseparable element of the continuum of violence, interlinked with various other types and embodiments of harm that transcend the archaic boundaries of online and offline realms. To adequately address the increasingly complex nature of violence on digital platforms, it is paramount to understand how digital platforms may modify and amplify violent behavior and content. The initial step in such efforts is to contextualize violence within its cultural and historical settings. Accordingly, this panel explores digital manifestations of violence as experienced and perceived within specific socio-cultural contexts. The papers presented delve into four case studies from diverse socio-cultural contexts (Colombia, Northern Ireland, Israel/Palestine, and Syria), and touch upon pressing contemporary issues such as the Israeli-Palestinian conflict, anti-religion protest in Iran, and the recent earthquakes in Syria. These case studies aim to explore the impact and reach of various forms of violence manifested on digital platforms. Panelists unpack ways of inciting, reproducing, expressing, and countering violence, enacted through a wide variety of media practices (such as memes, tweets and viral videos) across various platforms (including TikTok, Facebook, Twitter, and WhatsApp). In this panel we seek to build bridges and deepen our understanding of the complex and multifaceted dynamics of digital violence by exploring possible solutions for mitigating its detrimental impact.
PRACTICES AND PARTICIPATION OF MARGINALISED YOUTH IN NON-FORMAL AND DIGITALISED EDUCATIONAL ARRANGEMENTS

Educational inequality is prevalent in Germany and depends on different levels of social, economic and cultural capital (Bourdieu 1987). Yet, non-formal and informal educational arrangements are increasingly considered relevant to tackle those inequalities, specifically when it comes digitalised societies (Jeong et al. 2018). Non-formal educational providers increasingly target marginalised youth to reduce educational inequalities. However, those programmes have scarcely been researched. The question emerges, whether non-formal digitialised educational arrangements succeed at enabling educational participation of marginalised youth. In comparing two non-formal educational institutions, the research project “DILABoration” identifies conditions under which marginalised youth are able to profit off of those providers’ programmes and reconstructs them on a subjective level. In an ethnographic and reconstructive approach, a) different conditions in non-formal educational arrangements, b) learning and educational processes, specifically respective digital media use as well as c) the accessibility of participation within those arrangements are being investigated from a marginalised youths’ perspective. In order to empirically reconstruct the mechanisms of educational participation, the participants’ and employees’ practices within those arrangements are examined through participatory observation and videography. The data is analysed through Grounded Theory Methodology (Strauss/Corbin) as well as Artefact Analysis (Lueger / Froschauer 2018). Situational Analysis (Clarke/Washburn/Friese 2018) is applied in order to visualise constellations and relations between different human and non-human entities. Additionally, group discussions with non-participants of the programmes help identifying conditions that enable or constrain participation.
EXPLORING FACEBOOK’S “WHY AM I SEEING THIS AD” FEATURE: MEANINGFUL TRANSPARENCY OR FURTHER OBFUSCATION?

For more than a decade, digital advertising has been the primary means of funding online content and services. The evolution of digital advertising towards algorithmically targeted advertising, believed to be highly personalized and tailored to the individual, has presented new challenges for public oversight. Whereas previously, public concern centred on the content of ads and their exposure to audiences, the rise of platform-based advertising means focus has shifted to the distribution of ads and how they reach us. In response to public concerns and regulatory pressures, companies such as Meta (the parent of Facebook) have introduced transparency tools for researchers and consumers to ‘explain’ the function of advertising on the platform, including the Ad Library and the “Why Am I Seeing This Ad” feature. Despite being a central feature of Meta’s response towards increasing external scrutiny, little is known about how the WAIST feature works, or how it operates at a population level. In response we offer a description of WAIST data collected at scale, informed from a nationwide citizen data donation project of Facebook advertising. We analyse this data with a view to better understand Meta’s algorithmic advertising system, and to inform questions regarding the sufficiency of WAIST as an algorithmic explanatory mechanism for users.
THE WEIRD GOVERNANCE OF FACT-CHECKING: FROM WATCHDOGS TO CONTENT MODERATORS

In this work, we chart the multiple conflicts between stakeholders in the pursuit of a common standard for fact-checking outside Western Industrialized Educated Rich and Educated (WEIRD) countries, a problem that sits at the center of the institutional mission of fact-checkers as watchdogs of politicians and enforcers of content moderation. We apply reflexive thematic analysis to a set of interviews with 37 fact-checking experts from 35 organizations in 27 countries to catalogue the methods employed by fact-checkers and the pressures they contend with in non-WEIRD countries. In contrast to the one-size-fits-all approach to community guidelines implemented by social platforms worldwide, our results show that the asymmetric relationship with platform companies compels fact-checkers to adjust their methods and strategies to account for the political and cultural dimensions driving mis- and disinformation in their local contexts. Our findings detail three ways through which social platforms impinge on the scope, values, and institutional mission of non-WEIRD fact-checking organizations. As we argue, the platformization of non-WEIRD fact-checkers entails a convoluted process in which social media platforms gradually nudge fact-checkers into becoming part of the content moderation industry, a shift that runs counter to the democracy-building values underpinning the fact-checking movement. We conclude with a discussion of our findings and recommendations for content moderation both in WEIRD and non-WEIRD contexts.
ALGORITHMIC FOLK THEORIES OF ONLINE HARASSMENT: HOW SOCIAL MEDIA ALGORITHMS ENABLE ONLINE HARASSMENT AND PREVENT INTERVENTION

Online harassment is a public health concern, and social media algorithms are often proposed as a solution by social media companies. As online harassment grows, there are concerns that algorithms as content moderators fail to achieve their desired effect because of inabilities to contextualize social issues. This research contributes to the intersection of algorithms and online harassment by investigating the algorithmic folk theories of the victims, perpetrators, and bystanders of online harassment. Strategically sampling the experiences of marginalized identity categories who experienced harassment, we conducted grounded theory interviews and found that people theorize that algorithmic failures fuel online harassment and isolate victims. We describe four folk theories that victims, perpetrators, and witnesses utilize to make sense of their experiences of online harassment. The $2 asserts algorithms only pay attention to harassment incidents with a large number of flags. $2 describes perceptions of how algorithms amplify harassment content to increase engagement. $2 refers to perceptions that algorithms seek to form new audiences for content, which networks harassers together. $2 finds victims perceive that algorithms fail to contextualize the harassment of marginalized communities. Victims, bystanders, and perpetrators each described using their folk theories to instigate, push back, or succumb to the culture of online harassment. Understanding these algorithmic online harassment folk theories highlights how social media algorithms perpetuate harassment and fail to support victims. $2 : algorithmic folk theories, online harassment, networked harassment, social media, content moderation.
IDENTIFYING WITH PRIVACY: REFERENCES TO PRIVACY IN DEVELOPERS’ GITHUB PROFILES

The proposed presentation explores the ways in which developers use the notion of privacy in their GitHub biographies. Initially a code-sharing platform, GitHub has become in recent years a major recruitment site. In that environment, a valuable potential worker is one who knows how to extract and make the most of users’ personal information. At the same time, in an open source platform, “privacy” maintains its romantic allure as a worthwhile endeavor. How do developers manage this tension? And what may its negotiation imply for the production of privacy? Analyzing the 2025 GitHub bios in which developers use the word “privacy,” we explore two articulations of privacy in developers’ self-presentation: privacy as a profession, and privacy as a passion.
THE INFRASTRUCTURAL POWER OF PROGRAMMATIC ADVERTISING NETWORKS: ANALYZING DISINFORMATION INDUSTRIES IN BRAZIL

The informational disorder that sprawls through multiple state-nations, exacerbated by violent uprisings and coup attempts such as the US Capitol Storm on January 6th, 2021 and the Brazilian coup attempt on January 8th, 2023, has shed new light on the political economy of disinformation industries. In particular, it brings to the forefront the problem of economic incentives for creating and spreading disinformation. This paper builds on critical platform and infrastructure research literature for analyzing the multilateral infrastructural power of programmatic advertising networks. Our research questions are: how is power exercised by programmatic advertising infrastructures while managing its multilateral relationships? In terms of technicities, governance and business models, how does these infrastructures enable or reinforce desinformation disorder? This case study draws on a multi-methodological approach, combining digital methods research and critical analysis of platform documents. The empirical data obtained has 95.269 ads collected on the website (data scraped with a Python script developed by one of the authors) during the election month. Empirical data show that MGID, a native advertising platform, placed 54% of the advertisements on Terra Brasil Notícias. Google Ads was the second largest provider of digital ads on TBN, despite its policy`s restrictions on sellers that host unreliable or harmful content on issues such as health, climate, elections and democracy. Findings from the Brazilian case also contribute to understanding the infrastructural power of big tech governing the monetization of publishers in the Global South.