Author: Latest Results

Affective, defective, and infective narratives on social media about nuclear energy and atomic conflict during the 2022 Italian electoral campaign

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Abstract

In the digital age, poor public communication catalyzes the spread of disinformation within public opinion. Anyone can produce political content that can reach a global audience, and social media has become a vital tool for political leaders to convey messages to the electorate. The 2022 Italian election campaign has seen the term “nuclear” debated with two different declinations: on the one hand, regarding nuclear energy for civilian use, and on the other hand, regarding the fear of an escalation of the conflict in Ukraine and the use of atomic weapons. This research aims to analyze the social media debate by exploring multiplatform dynamics to qualitatively identify and analyze the connections between social media platforms that we have termed Bridges, a concept drawn from Transmedia Theory to describe the narrative relationship between platforms. The methodological approach will follow an explanatory sequential design that will rely on digital methods to identify connections between platforms (bridges) and then apply an exploratory qualitative approach to enrich the data and capture the nuances of the debate. As expected, we found polarized positions and fragmentation on both issues of civilian nuclear energy and the atomic conflict narrative. Primary evidence shows bridges spreading affective, defective, and infective content across platforms in a multifaceted social media ecosystem. Affective refers to rhetoric that appeals to people’s feelings. Defective means the discussion that brings attention to hyper-partisan news channels, fake news, and misinformation. Infective means bridges with below-the-radar platforms, niche channels, or pseudo-information channels. They use bridges with mainstream platforms to gain the potential to go viral. The paper highlights the importance of cross-platform and interdisciplinary approaches to addressing disinformation in a media ecosystem where social media plays an increasing role in a country’s democratic dynamics.

Affective, defective, and infective narratives on social media about nuclear energy and atomic conflict during the 2022 Italian electoral campaign

Latest Results

Abstract

In the digital age, poor public communication catalyzes the spread of disinformation within public opinion. Anyone can produce political content that can reach a global audience, and social media has become a vital tool for political leaders to convey messages to the electorate. The 2022 Italian election campaign has seen the term “nuclear” debated with two different declinations: on the one hand, regarding nuclear energy for civilian use, and on the other hand, regarding the fear of an escalation of the conflict in Ukraine and the use of atomic weapons. This research aims to analyze the social media debate by exploring multiplatform dynamics to qualitatively identify and analyze the connections between social media platforms that we have termed Bridges, a concept drawn from Transmedia Theory to describe the narrative relationship between platforms. The methodological approach will follow an explanatory sequential design that will rely on digital methods to identify connections between platforms (bridges) and then apply an exploratory qualitative approach to enrich the data and capture the nuances of the debate. As expected, we found polarized positions and fragmentation on both issues of civilian nuclear energy and the atomic conflict narrative. Primary evidence shows bridges spreading affective, defective, and infective content across platforms in a multifaceted social media ecosystem. Affective refers to rhetoric that appeals to people’s feelings. Defective means the discussion that brings attention to hyper-partisan news channels, fake news, and misinformation. Infective means bridges with below-the-radar platforms, niche channels, or pseudo-information channels. They use bridges with mainstream platforms to gain the potential to go viral. The paper highlights the importance of cross-platform and interdisciplinary approaches to addressing disinformation in a media ecosystem where social media plays an increasing role in a country’s democratic dynamics.

Microstructure evolution and mechanical properties of Ti-15-3 alloy joint fabricated by submerged friction stir welding

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Abstract

In this work, the Ti-15-3 alloy joints were successfully prepared via submerged friction stir welding (SFSW) for the first time. The microstructure evolutions and mechanical properties of the SFSW joints were characterized by electron backscattering diffraction, finite element simulation, microhardness, and tensile tests. The results revealed that the joint included three distinct zones, named as stirring zone (SZ), thermo-mechanically affected zone (TMAZ), and base metal (BM), respectively. During SFSW, the peak temperature (~ 808 °C) and strain in SZ gradually decreased from the upper surface to the bottom surface along the thickness of the as-received plate. Meanwhile, the temperature and strain on the advancing side (AS) were higher than that of the retreating side (RS) within SZ. Comparatively, a slightly low temperature (~ 480 °C) and strain occurred in TMAZ. Due to the high temperature and large strain during SFSW, the grains were significantly refined, and the major grain refinement mechanism of SZ was continuous dynamic recrystallization, while that of TMAZ was coupled by continuous and discontinuous dynamic recrystallization. Note that the ideal shear texture formed in SZ. The shear textures components at the top, center, and bottom of SZ center were D2( \(11\overline{2 }\) )[111], while that of AS and RS within SZ was D1( \(11\overline{2 }\) )[111]. Finally, the ultimate tensile strengths of SZ and TMAZ were 854 MPa and 816 MPa, which reached that of 103% and 96% of BM, respectively. In summary, it was an effective method to prepare a uniform and high-performance Ti-15-3 alloy joint through SFSW.

Assessment and modelling of hydro-sedimentological flows of the eastern river Dhauliganga, north-western Himalaya, India

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Abstract

Assessment and modelling of hydro-sedimentological flows of a high-altitude river system is a critical step for developing and managing sustainable water resource projects and best management practices (BMPs) in the downslope regions of the Indian Himalayan Region (IHR). A field study was carried out to measure the hydraulic parameters such as water pressure, water flow rate, and stage of the 6th order glacier-fed river to quantify hydro-sedimentological flows using area-velocity and vacuum filtration method for 3 successive years during 2018–2020. Further, a process-based hydrological model: Soil and Water Assessment Tool (SWAT), is used to simulate the hydro-sedimentological flows. The statistical indices such as coefficient of determination (R2), Nash–Sutcliffe efficiency (NSE), and percentage bias (PBIAS) attain higher values during both calibration and validation periods. The snowmelt and rainfall contributions to the total streamflow range from 17–35 % and 27–34 %, respectively. The measured and modelled hydro-sedimentological flows show high variability with a high coefficient of variation (COV > 1). However, the mean suspended sediment load (SSL) carried by the river was low compared to the other glacier-fed rivers. The physical weathering rate (PWR) dominates the chemical weathering rate (CWR) for the study years. This might be due to higher crushing of the region and weathering of base rock materials. The PWR and CWR of the basin are less than that of the western Himalayan regions. This study also underscores the necessity of basin management plans in the Himalaya, emphasizing erosion identification, snowmelt and glacier melt in streamflow, and customized groundwater recharge strategies through GIS mapping, providing essential insights for sustainable land and water resource management in changing climatic conditions.

Advances in materials informatics: a review

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Abstract

Materials informatics (MI) is aimed to accelerate the materials discovery using computational intelligence and data science. Progress of MI depends on the strength of database and artificial intelligence protocols comprising machine learning (ML) and deep learning (DL) frameworks. Conventional ML models are simple and interpretable, relying on statistical techniques and algorithms to learn patterns and make predictions with limited data. Conversely, DL, an advancement of ML, employs mathematical neural networks to automatically extract features and handle intricate data at the cost of data size and computational complexity. This work aims to provide a state-of-the-art understanding of the tools, data sources and techniques used in MI and their benefits and challenges. We evaluate the growth of MI through its subfields and track the main path of its advancement for artificial intelligence-driven materials discovery. The advancements in computational intelligence via machine learning and deep learning algorithms in different fields of materials science are discussed. As a specific example, understanding of materials properties using microstructural images is reviewed. Future demands and research prospects in materials science utilizing materials informatics have also been comprehensively analyzed.

Assessing the risks and opportunities posed by AI-enhanced influence operations on social media

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Abstract

Large language models (LLMs) like GPT-4 have the potential to dramatically change the landscape of influence operations. They can generate persuasive, tailored content at scale, making campaigns using falsified content, such as disinformation and fake accounts, easier to produce. Advances in self-hosted open-source models have meant that adversaries can evade content moderation and security checks built into large commercial models such as those commercialised by Anthropic, Google, and OpenAI. New multi-lingual models make it easier than ever for foreign adversaries to pose as local actors. This article examines the heightened threats posed by synthetic media, as well as the potential that these tools hold for creating effective countermeasures. It begins with assessing the challenges posed by a toxic combination of automated bots, human-controlled troll accounts, and more targeted social engineering operations. However, the second part of the article assesses the potential for these same tools to improve detection. Promising countermeasures include running internal generative models to bolster training data for internal classifiers, detecting statistical anomalies, identifying output from common prompts, and building specialised classifiers optimised for specific monitoring needs.

Keeping it authentic: the social footprint of the trolls’ network

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Abstract

In 2016, a network of social media accounts animated by Russian operatives attempted to divert political discourse within the American public around the presidential elections. This was a coordinated effort, part of a Russian-led complex information operation. Utilizing the anonymity and outreach of social media platforms Russian operatives created an online astroturf that is in direct contact with regular Americans, promoting Russian agenda and goals. The elusiveness of this type of adversarial approach rendered security agencies helpless, stressing the unique challenges this type of intervention presents. Building on existing scholarship on the functions within influence networks on social media, we suggest a new approach to map those types of operations. We argue that pretending to be legitimate social actors obliges the network to adhere to social expectations, leaving a social footprint. To test the robustness of this social footprint, we train artificial intelligence to identify it and create a predictive model. We use Twitter data identified as part of the Russian influence network for training the artificial intelligence and to test the prediction. Our model attains 88% prediction accuracy for the test set. Testing our prediction on two additional models results in 90.7% and 90.5% accuracy, validating our model. The predictive and validation results suggest that building a machine learning model around social functions within the Russian influence network can be used to map its actors and functions.

Language revitalization through a social movement lens: grassroots Galician language activism

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Abstract

In this article, a social movement lens is applied to examine the dynamics of an urbanbased language revitalization movement in the Autonomous Community of Galicia (North-western Spain). The potential of Resource Management Theory is explored as a way of systematically analysing the dynamics of urban-based language revitalization movements. It does this by identifying factors which both helped fuel the emergence and growth of this Galician grassroots movement as well as those constraining its potential development. Drawing on in-depth interviews and observations collected over six months of ethnographic fieldwork in one of Galicia’s main cities, social movement theory is used to analyse the role of Galician social movement activists as social agents in shaping the success of their language revitalization initiative. We argue that a social movement lens provides a useful analytical toolkit to focus on the grassroots efforts of social agents involved in peripheral ethnolinguistic mobilization in minority language contexts such as Galicia. Ultimately, we aim to show that these social movement revitalization initiatives go beyond language as an object and are centred around language-based struggles which not only address strategy dilemmas but also scaffold social relations and ties among speakers as they mobilize within particular institutional fields.