Public texts aiming at reader manipulation for propaganda or disinformation purposes pose a significant threat to society. The ability to detect the presence of a specific manipulative technique in a ...text offers an informed warning to readers and guides them to carefully judge the actual statement.
In this article, we address the problem of developing new models capable of analyzing newspaper articles for propagandistic features. We introduce a new large dataset of manipulative techniques obtained via gathering and human annotation of 8,646 newspaper articles in Czech, which represents one of the former Soviet influence area languages. The dataset allows both to train new methods to recognize propaganda and disinformation and offer a general comparable benchmark for the techniques. We evaluate the dataset against selected state-of-the-art machine learning approaches to provide high-performing baselines for detecting seventeen annotated manipulative techniques. We also present thorough measurements of inter-annotator agreements that approximate the difficulty level of each of the attributes.
As a new finding, we propose a set of text style analysis features that lean on the assumption that each manipulation leads to a specific style pattern. We show that the style analysis improves the detection results for most of the manipulative techniques. The viability of the approach is also confirmed on the well-known QProp propaganda dataset, providing new state-of-the-art results.
•New publicly available Propaganda dataset of 8,646 Czech newspaper articles.•Human-annotated for 18 manipulative techniques used in propaganda and disinformation.•Evaluation of the Propaganda dataset with state-of-the-art techniques.•Style-specific manipulation detectors, comparison with content-only text analysis.•The style analysis improved the results in 15 of 17 evaluated manipulative techniques.
This paper analyses the risk-adjusted performance of Islamic and conventional equity funds during the COVID-19 pandemic. We show that Islamic equity funds demonstrated differentials in risk-adjusted ...performance, investment styles, and volatility timing compared to their conventional counterparts. Specifically, the results revealed that Islamic equity funds are more resilient to COVID-19 shock since they outperformed non-Islamic peers during the peak months of the pandemic. The trend continues even when the spread smoothens. These findings confirm the safe-haven properties of Islamic equity funds, which is helpful for investors aiming to hedge pandemic risks. The style analysis reveals investment drift from riskier styles to more prudent options in response to each stage's uncertainties. The results suggest policymakers should further investigate Islamic financial assets and their underlying principles to improve the resilience of economic systems in any future black swan events.
•We compare the risk adjusted performance of 476 Islamic and 591 conventional equity funds.•Islamic equity funds outperformed non-Islamic counterparts during the peak months of the COVID-19 pandemic.•Islamic equity funds are safe havens.•We report the drift from riskier styles to more prudent options for all Islamic equity funds.•We document the evidence of volatility timing for Islamic equity funds.
Understanding what groups stand for is integral to a diverse array of social processes, ranging from understanding political conflicts to organisational behaviour to promoting public health ...behaviours. Traditionally, researchers rely on self-report methods such as interviews and surveys to assess groups’ collective self-understandings. Here, we demonstrate the value of using naturally occurring online textual data to map the similarities and differences between real-world groups’ collective self-understandings. We use machine learning algorithms to assess similarities between 15 diverse online groups’ linguistic style, and then use multidimensional scaling to map the groups in two-dimensonal space (
N
=1,779,098 Reddit comments). We then use agglomerative and k-means clustering techniques to assess how the 15 groups cluster, finding there are four behaviourally distinct group types – vocational, collective action (comprising political and ethnic/religious identities), relational and stigmatised groups, with stigmatised groups having a less distinctive behavioural profile than the other group types. Study 2 is a secondary data analysis where we find strong relationships between the coordinates of each group in multidimensional space and the groups’ values. In Study 3, we demonstrate how this approach can be used to track the development of groups’ collective self-understandings over time. Using transgender Reddit data (
N
= 1,095,620 comments) as a proof-of-concept, we track the gradual politicisation of the transgender group over the past decade. The automaticity of this methodology renders it advantageous for monitoring multiple online groups simultaneously. This approach has implications for both governmental agencies and social researchers more generally. Future research avenues and applications are discussed.
When geometric models with a desired combination of style and functionality are not available, they currently need to be created manually. We facilitate algorithmic synthesis of 3D models of man-made ...shapes which combines user-specified style, described via an exemplar shape, and functionality, encoded by a functionally different target shape. Our method automatically transfers the style of the exemplar to the target, creating the desired combination. The main challenge in performing cross-functional style transfer is to implicitly separate an object's style from its function: while stylistically the output shapes should be as close as possible to the exemplar, their original functionality and structure, as encoded by the target, should be strictly preserved. Recent literature point to the presence of similarly shaped, salient geometric elements as a main indicator of stylistic similarity between 3D shapes. We therefore transfer the exemplar style to the target via a sequence of element-level operations. We allow only compatible operations, ones that do not affect the target functionality. To this end, we introduce a cross-structural element compatibility metric that estimates the impact of each operation on the edited shape. Our metric is based on the global context and coarse geometry of evaluated elements, and is trained on databases of 3D objects. We use this metric to cast style transfer as a tabu search, which incrementally updates the target shape using compatible operations, progressively increasing its style similarity to the exemplar while strictly maintaining its functionality at each step. We evaluate our framework across a range of man-made objects including furniture, light fixtures, and tableware, and perform a number of user studies confirming that it produces convincing outputs combining the desired style and function.
El estudio de las habilidades personales ha ido tomando mayor relevancia en los últimos años en todos los ámbitos; probablemente debido a que son fundamentales para el trabajo en equipo, además de ...ser clave para el éxito en la vida profesional a nivel individual. Sin embargo, a diferencia de las habilidades técnicas, determinar si una persona posee o domina habilidades personales no es una tarea trivial. Actualmente los mecanismos más comunes de valoración son la entrevista y los instrumentos psicométricos, a los que se ha unido como alternativa el uso de videojuegos. Los videojuegos presentan la ventaja de abstraer al participante, haciéndolo olvidar que está siendo observado, generando un ambiente en el que se espera un desempeño más natural. En este contexto, existen diversas propuestas que se han tomado como base para generar un proceso para analizar habilidades personales mediante videojuegos.
•A method for classifying ancient paintings in chronology.•A bag-of-visual-words approach that uses appearance features and shape features.•A deep-learning network that refines the appearance ...features.•Algorithm evaluation using a collection of 660 ancient painting images.
Ancient paintings are valuable for historians and archeologists to study the humanities, customs and economy of the corresponding eras. For this purpose, it is important to first determine the era in which a painting was drawn. This problem can be very challenging when the paintings from different eras present a same topic and only show subtle difference in terms of the painting styles. In this paper, we propose a novel computational approach to address this problem by using the appearance and shape features extracted from the paintings. In this approach, we first extract the appearance and shape features using the SIFT and kAS descriptors, respectively. We then encode these features with deep learning in an unsupervised way. Finally, we combine all the features in the form of bag-of-visual-words and train a classifier in a supervised fashion. In the experiments, we collect 660 Flying-Apsaras paintings from Mogao Grottoes in Dunhuang, China and classify them into three different eras, with very promising results.
We present a
semi-supervised co-analysis
method for learning 3D shape styles from
projected feature lines
, achieving style patch
localization
with only weak supervision. Given a collection of 3D ...shapes spanning multiple object categories and styles, we perform style co-analysis over projected feature lines of each 3D shape and then back-project the learned style features onto the 3D shapes. Our core analysis pipeline starts with mid-level patch sampling and pre-selection of candidate style patches. Projective features are then encoded via patch convolution. Multi-view feature integration and style clustering are carried out under the framework of
partially shared latent factor
(PSLF) learning, a multi-view feature learning scheme. PSLF achieves effective multi-view feature fusion by distilling and exploiting consistent and complementary feature information from multiple views, while also selecting style patches from the candidates. Our style analysis approach supports both unsupervised and semi-supervised analysis. For the latter, our method accepts both user-specified shape labels and style-ranked triplets as clustering constraints. We demonstrate results from 3D shape style analysis and patch localization as well as improvements over state-of-the-art methods. We also present several applications enabled by our style analysis.
In musicology, there has been a long debate about a meaningful partitioning and description of music history regarding composition styles. Particularly, concepts of historical periods have been ...criticized since they cannot account for the continuous and interwoven evolution of style. To systematically study this evolution, large corpora are necessary suggesting the use of computational strategies. This article presents such strategies and experiments relying on a dataset of 2000 audio recordings, which cover more than 300 years of music history. From the recordings, we extract different tonal features. We propose a method to visualize these features over the course of history using evolution curves. With the curves, we re-trace hypotheses concerning the evolution of chord transitions, intervals, and tonal complexity. Furthermore, we perform unsupervised clustering of recordings across composition years, individual pieces, and composers. In these studies, we found independent evidence of historical periods that broadly agrees with traditional views as well as recent data-driven experiments. This shows that computational experiments can provide novel insights into the evolution of styles.
In this study, we examine investment style, and style consistency and its relationship with risk-adjusted performance of the Indian fixed income mutual funds (MFs) using a sample of 242 funds across ...16 categories over a period from April 2015 to March 2020. Our findings indicate that (a) fund managers practice securities selection, but their securities selection ability fails to improve risk-adjusted returns; (b) higher style consistency leads to better risk-adjusted performance; and (c) investment style and style consistency have considerable impact on fund performance. This is possibly the first comprehensive study that analyses investment style and its relationship with the performance of Indian fixed income MFs and contributes to the growing body of research on performance evaluation of fixed income funds.
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