Intelligent agents powered by large language models (LLMs) have recently demonstrated impressive capabilities and gained increasing popularity on social media platforms. While LLM agents are reshaping the ecology of social media, there exists a current gap in conducting a comprehensive evaluation of their ability to comprehend media content, understand user behaviors, and make intricate decisions. To address this challenge, we introduce SoMe, a pioneering benchmark designed to evaluate social media agents equipped with various agent tools for accessing and analyzing social media data. SoMe comprises a diverse collection of 8 social media agent tasks, 9,164,284 posts, 6,591 user profiles, and 25,686 reports from various social media platforms and external websites, with 17,869 meticulously annotated task queries. Compared with the existing datasets and benchmarks for social media tasks, SoMe is the first to provide a versatile and realistic platform for LLM-based social media agents to handle diverse social media tasks. By extensive quantitative and qualitative analysis, we provide the first overview insight into the performance of mainstream agentic LLMs in realistic social media environments and identify several limitations. Our evaluation reveals that both the current closed-source and open-source LLMs cannot handle social media agent tasks satisfactorily. SoMe provides a challenging yet meaningful testbed for future social media agents. Our code and data are available at https://github.com/LivXue/SoMe
Fleets of networked manufacturing machines of the same type, that are collocated or geographically distributed, are growing in popularity. An excellent example is the rise of 3D printing farms, which consist of multiple networked 3D printers operating in parallel, enabling faster production and efficient mass customization. However, optimizing process parameters across a fleet of manufacturing machines, even of the same type, remains a challenge due to machine-to-machine variability. Traditional trial-and-error approaches are inefficient, requiring extensive testing to determine optimal process parameters for an entire fleet. In this work, we introduce a machine learning-based collaborative recommender system that optimizes process parameters for each machine in a fleet by modeling the problem as a sequential matrix completion task. Our approach leverages spectral clustering and alternating least squares to iteratively refine parameter predictions, enabling real-time collaboration among the machines in a fleet while minimizing the number of experimental trials. We validate our method using a mini 3D printing farm consisting of ten 3D printers for which we optimize acceleration and speed settings to maximize print quality and productivity. Our approach achieves significantly faster convergence to optimal process parameters compared to non-collaborative matrix completion.
Patrick R. Bishop, Summer Chenoweth, Emmanuel Fleurantin
et al.
Invariant manifolds are one of the key features that organize the dynamics of a differential equation. We introduce a novel approach to visualizing and studying invariant manifolds by using 3D printing technology, combining advanced computational techniques with modern 3D printing processes to transform mathematical abstractions into tangible models. Our work addresses the challenges of translating complex manifolds into printable meshes, showcasing results for the following systems of differential equations: the Lorenz system, the Arneodo-Coullet-Tresser system, and the Langford system. By bridging abstract mathematics and physical reality, this approach promises new tools for research and education in nonlinear dynamics. We conclude with practical guidelines for reproducing and extending our results, emphasizing the potential of 3D-printed manifolds to enhance understanding and exploration in dynamical systems theory.
L. S. Taillie, E. Busey, Fernanda Mediano Stoltze
et al.
Reducing children's exposure to food marketing is an important obesity prevention strategy. This narrative review describes current statutory regulations that restrict food marketing; reviews available evidence on the effects of these regulations; and compares policy design elements in Chile and the United Kingdom. Currently, 16 countries have statutory regulations on unhealthy food marketing to children. Restrictions on television advertising, primarily during children's programming, are most common. Schools are also a common setting for restrictions. Regulations on media such as cinema, mobile phone applications, print, packaging, and the internet are uncommon. Eleven evaluations of policies in 4 jurisdictions found small or no policy-related reductions in unhealthy food advertising, in part because marketing shifted to other programs or venues; however, not all policies have been evaluated. Compared with the United Kingdom, Chile restricts marketing on more products, across a wider range of media, using more marketing techniques. Future research should examine which elements of food marketing policy design are most effective at reducing children's exposure to unhealthy food marketing.
This paper presents a case study of how Petco, a leading pet retailer, innovated their pet health analysis processes using the Media Insights Engine to reduce the time to first diagnosis. The company leveraged this framework to build custom applications for advanced computer vision tasks, such as identifying potential health issues in pet videos and images, and validating AI outcomes with pre-built veterinary diagnoses. The Media Insights Engine provides a modular and extensible solution that enabled Petco to quickly build machine learning applications for media workloads. By utilizing this framework, Petco was able to accelerate their project development, improve the efficiency of their pet health analysis, and ultimately reduce the time to first diagnosis for pet health issues. This paper discusses the challenges of pet health analysis using media, the benefits of using the Media Insights Engine, and the architecture of Petco's custom applications built using this framework.
Taking and posting selfies is a popular activity, with some individuals taking and sharing multiple selfies each day. The influence of the selfie angle, as opposed to more traditional photo angles such as the allocentric images we see in print media, on our aesthetic judgements of images of bodies has not been explored. This study compared the attractiveness and weight judgements that participants made of images of the same bodies taken from different visual angles over a series of four experiments (total N = 272). We considered how these judgements may relate to disordered eating thoughts and behaviours. Selfies were judged to be slimmer than images from other perspectives, and egocentric images were judged to be the least attractive. The way participants rated bodies seen from different perspectives was related to their own disordered eating thoughts and behaviours. These results contribute to our understanding of how we perceive the images we see on social media and how these might be related to how we feel about our own and other people's bodies.
Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.
Михайло Петрович Горський, Клавдія Юріївна Зенкова , Вікторія Валеріївна Морфлюк-Щур
et al.
We have proposed a new polarimetric method of Stokes polarimetry, which is more informative in terms of representing optically inhomogeneous structures by spatially coherent filtering. The idea of this approach is based on the spatial-frequency filtering of polarization images of optically inhomogeneous networks of polymer layers. The results of the development and experimental testing of a new Fourier method of polarimetry of spectral-frequency laser images, which are based on a statistical analysis of polarization distributions with the determination of a set of statistical parameters, are presented. The statistical moments of the fourth order are given, which characterize the distributions of the azimuth and ellipticity of the polarization. The presented experimental methods have shown the presence of a polarization-inhomogeneous structure of FOMC of real polycrystalline sets of polymers, and also confirmed the adequacy of the proposed model representation of their optically inhomogeneous properties. It is shown that different types of the birefringent structure of polymers of different types are characterized by different values of the 1st–4th order static moments. The difference between them reaches 3–4 times. A relationship is found between a set of statistical moments of the 1st - 4th orders, which characterize the coordinate distributions of the azimuth and ellipticity of the Fourier spectra of polyethylene samples, as well as the parameters of the optical anisotropy of such grids. The reason for the formation of the polarization structure of the Fourier spectra of laser images of polymer networks is the superposition of differently polarized partial coherent waves, which are formed by different-scale partial optically inhomogeneous molecular networks. The main mechanisms for the formation of azimuthal polarization distributions of the Fourier image are the ratios between 0–90 amplitude components, which are determined by the features of the spatial-frequency spectra of molecular crystals with different types of birefringence. The main mechanisms in the formation of distributions of polarization ellipticity maps are phase modulation, which is due to both the birefringence of molecular crystals and the optical characteristics of interfering partial waves.
Critical political economy of the media investigates how changes in the array of forces that exercise control of media institutions liberate or limit the public sphere. South Africa’s political economy of transition from apartheid to democracy was notably characterised by the emergence of new black capitalist interests merging with established white capital to refashion multi-racial capital, facilitated by a Black Economic Empowerment (BEE) strategy that aimed to address the racial injustices of the past. These changes greatly impacted media ownership, diversifying a previously racially homogenised and localised apartheid media market. The ‘new’ South Africa also moved from racial capitalism to neoliberalism as its economic system. This study investigates whether these ownership diversity changes in the South African print media market in the first twenty years of its democracy (1994–2014) liberated or limited the public sphere. A total of 684 newspaper front-page and editorial articles were analysed using both quantitative and qualitative content analysis, to aggregate as well as investigate deeper meanings and associations in diversity trends. South Africa’s neoliberal economic context substantially informs the nature of print media content transformation and diversity, which is found to be elite driven, marking the emergence of class continuities. This despite a specialised BEE affirmative action programme envisaged to cultivate transformed and diverse media content through transformed ownership. The study concludes that attempts to transform, diversify, de-westernise, and decolonise the media systems in post-colonial countries will be futile if the power of neoliberalism to perpetuate class inequalities and the race dynamics of the past remain underestimated and unaddressed.
Communication. Mass media, Communities. Classes. Races
Three-dimensional (3D) technologies have been developing rapidly recent years, and have influenced industrial, medical, cultural, and many other fields. In this paper, we introduce an automatic 3D human head scanning-printing system, which provides a complete pipeline to scan, reconstruct, select, and finally print out physical 3D human heads. To enhance the accuracy of our system, we developed a consumer-grade composite sensor (including a gyroscope, an accelerometer, a digital compass, and a Kinect v2 depth sensor) as our sensing device. This sensing device is then mounted on a robot, which automatically rotates around the human subject with approximate 1-meter radius, to capture the full-view information. The data streams are further processed and fused into a 3D model of the subject using a tablet located on the robot. In addition, an automatic selection method, based on our specific system configurations, is proposed to select the head portion. We evaluated the accuracy of the proposed system by comparing our generated 3D head models, from both standard human head model and real human subjects, with the ones reconstructed from FastSCAN and Cyberware commercial laser scanning systems through computing and visualizing Hausdorff distances. Computational cost is also provided to further assess our proposed system.
In this paper, we address the problem of modeling a printing-imaging channel built on a machine learning approach a.k.a. digital twin for anti-counterfeiting applications based on copy detection patterns (CDP). The digital twin is formulated on an information-theoretic framework called Turbo that uses variational approximations of mutual information developed for both encoder and decoder in a two-directional information passage. The proposed model generalizes several state-of-the-art architectures such as adversarial autoencoder (AAE), CycleGAN and adversarial latent space autoencoder (ALAE). This model can be applied to any type of printing and imaging and it only requires training data consisting of digital templates or artworks that are sent to a printing device and data acquired by an imaging device. Moreover, these data can be paired, unpaired or hybrid paired-unpaired which makes the proposed architecture very flexible and scalable to many practical setups. We demonstrate the impact of various architectural factors, metrics and discriminators on the overall system performance in the task of generation/prediction of printed CDP from their digital counterparts and vice versa. We also compare the proposed system with several state-of-the-art methods used for image-to-image translation applications.
In this paper we investigate what folk models of misinformation exist through semi-structured interviews with a sample of 235 social media users. Work on social media misinformation does not investigate how ordinary users - the target of misinformation - deal with it; rather, the focus is mostly on the anxiety, tensions, or divisions misinformation creates. Studying the aspects of creation, diffusion and amplification also overlooks how misinformation is internalized by users on social media and thus is quick to prescribe "inoculation" strategies for the presumed lack of immunity to misinformation. How users grapple with social media content to develop "natural immunity" as a precursor to misinformation resilience remains an open question. We have identified at least five folk models that conceptualize misinformation as either: political (counter)argumentation, out-of-context narratives, inherently fallacious information, external propaganda, or simply entertainment. We use the rich conceptualizations embodied in these folk models to uncover how social media users minimize adverse reactions to misinformation encounters in their everyday lives.
Noise is the non-target search results that people encounter when searching for a particular topic of interest; it is also the cloud of distracting data that can obscure or deflect conservation communication. Online noise associated with large carnivores is particularly dense because their defining characteristics make them salient. Mountain lions (Puma concolor) exemplify noise associated with multiple vernaculars for a species in the crosshairs of conservation conundrums. We compared internet search results, Google Trends reflecting topic interest, use in science publications and sentiment in print and online media for P. concolor's most frequent vernacular names, “mountain lion,” “cougar,” “puma” and “Florida panther.” Puma and panther exhibited greater noise and salience than cougar or mountain lion, but, results for mountain lion, followed by cougar, yielded the highest biological relevance. Online sentiment negatively correlated with biological relevance, with positive sentiment highest for the noisiest vernaculars, puma and panther. As conservation practitioners, we must recognize that public outreach is part of our scientific agenda and be conscious of crafting communication that reaches and resonates with our intended audiences.
General. Including nature conservation, geographical distribution
The Yorubà News, published by Obasa ̣, co-pioneered journalism, nay publishing, in Yorùbá language in southwestern Nigeria. Based in Ìbàdan and ̀ published by Ìlarè Printers, ̣́ The Yorùbá News, a bi-lingual serial in English and Yorùbá languages, remarkably had varying contents and wide circulation covering its locale, Íbàdan significantly; the southern protectorate, particularly Yorùbá land appreciably; and the entire nation, Nigeria marginally. Published weekly, Obasa – the Editor and Proprietor – successfully edited ̣ The Yorubà News ́ for over two decades from 1924 – 1945 when he died. Adopting the Diffusion of Innovations theory, this study examines the form and content of the newspaper. The form examines the structure and layout of the newspaper while the content discusses and evaluates issues covered in the publication. The form of The Yorùbá News is discussed in the context of the print media as a periodical by taking technical cognizance of its physical features: format, design and layout, typography, columns, paper, size and production quality. Content-wise, the paper exhaustively describes the subject matters of The Yorubà News ́ by dwelling critically on the issues raised, examining in details and critiquing its recurrent subject matters notably: the news stories, editorials, cover, advertorials, news and notes, etc. The inter-dependence of form and content is also examined to bring to the fore the social, cultural, political, and economic values of the maiden Yorùbá Newspaper: The Yorùbá News.
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This study investigates the syntactic structuration of the five select Hiligaynun Visayan Print Media Articles, namely: three news articles such as WANTED PERSON SA ANTIQUE, PRISU, LALAKI GINSIRBIHAN WARRANT, 4- KA TU-IG NGA BATA NALUMUS SA SUBA, and two editorials: SWILDU SANG MANUNUDLU and SAKA SA KUNTRIBUSYUN SA SSS. The study focuses on the following linguistic aspects (1) Immediate Constituents Analysis of the sentences, (2) phrase structures of the syntactic forms, and (3) pedagogic grammar monograph on Visayan Hiligaynun Syntax. The syntactic analysis through Immediate Constituents was the method used for the structuration segments of the articles. The sentences were analyzed using the constituent analysis to reveal syntactic structuration. ICA (Immediate Constituent Analysis) has two phases: phase one, identifying immediate constituents, and phase two, the rule in determining the syntactic structure. Based on the ICA and phrase structures of the sentences, the following are the findings: The ICA of the articles reveals that out of the nineteen sentences, eight are composed of simple sentences, eight compound sentences, and three compound-complex sentences. The syntactic structure phrasal analyses of the five articles yield nineteen sentences consisting of noun phrases, verb phrases, and prepositional phrases. A pedagogic grammar on Hiligaynun Visayan syntax is developed to guide the teaching and learning of Hiligaynun.
Rashmi Shivni, Christina Cline, Morgan Newport
et al.
Abstract Background Seminal reports, based on recommendations by educators, scientists, and in collaboration with students, have called for undergraduate curricula to engage students in some of the same practices as scientists—one of which is communicating science with a general, non-scientific audience (SciComm). Unfortunately, very little research has focused on helping students develop these skills. An important early step in creating effective and efficient curricula is understanding what baseline skills students have prior to instruction. Here, we used the Essential Elements for Effective Science Communication (EEES) framework to survey the SciComm skills of students in an environmental science course in which they had little SciComm training. Results Our analyses revealed that, despite not being given the framework, students included several of the 13 elements, especially those which were explicitly asked for in the assignment instructions. Students commonly targeted broad audiences composed of interested adults, aimed to increase the knowledge and awareness of their audience, and planned and executed remote projects using print on social media. Additionally, students demonstrated flexibility in their skills by slightly differing their choices depending on the context of the assignment, such as creating more engaging content than they had planned for. Conclusions The students exhibited several key baseline skills, even though they had minimal training on the best practices of SciComm; however, more support is required to help students become better communicators, and more work in different contexts may be beneficial to acquire additional perspectives on SciComm skills among a variety of science students. The few elements that were not well highlighted in the students’ projects may not have been as intuitive to novice communicators. Thus, we provide recommendations for how educators can help their undergraduate science students develop valuable, prescribed SciComm skills. Some of these recommendations include helping students determine the right audience for their communication project, providing opportunities for students to try multiple media types, determining the type of language that is appropriate for the audience, and encouraging students to aim for a mix of communication objectives. With this guidance, educators can better prepare their students to become a more open and communicative generation of scientists and citizens.