bluuebunny/crossref_metadata_2025 huggingface dataset:
Phytohormones mitigate salt stress damage in radish
Authors: Silva Toshik I. da , Dias Marlon G. , Lannes Samuel D. O. , Domingues Paloma , Sales Giuliana N. B. , Nóbrega Jackson S. , Ribeiro João E. da S. , Costa Franciscleudo B. da , Soares Lauriane A. dos A. , Lima Geovani S. de
Published: May 2024

ABSTRACT Phytohormones play a pivotal role in regulating plant growth and responding to salt stress, aiding in signal perception and defense system mediation. With this, the objective of the present study was to assess the impact of phytohormone application in mitigating the harmful effects of salt stress on radish. Three levels of NaCl (0, 50, and 100 mM) and five phytohormones (jasmonic acid, salicylic acid, cytokinin, gibberellin, and polyamine) plus a control treatment (deionized water) were studied. The application of phytohormones such as jasmonic acid and cytokinin improved photosynthetic efficiency, and diameter, length, and total soluble solids content of tuber. Under salt stress conditions, plants showed adaptations in gas exchange, varying their rates of photosynthesis and transpiration. Furthermore, an effective balance between carbon assimilation and water loss was observed in some plants. The application of phytohormones counteracted salt stress, safeguarding chlorophyll, sustaining gas exchange, and promoting plant growth of radish. Consequently, use of phytohormones represents an alternative for radish cultivation under salt stress.
Biostimulation on cotton growth and nutrient accumulation through inoculants based on endophytic fungi
Authors: Saboya Rita de C. C. , Santos Roseane C. dos , Freire Rosa M. M. , Albuquerque Fábio A. de , Pereira Rennan F. , Ramos Jean P. C. , Zilli Jerri É.
Published: May 2024

ABSTRACT Dark septate fungi (DSF) are endophytic microorganisms characterized by the formation of detached and melanized hyphae that confer adaptive advantages, including benefits in plant development. This study aimed to evaluate the nutrient accumulations, growth, and production of cotton plants inoculated with different DSF accessions. The experiment was carried out in a greenhouse in a completely randomized design with eight replicates. Cotton plants were grown in plastic bags containing commercial substrate and watered daily. Data were collected over the cycle of plants. Promising results were found in all inoculants; however, those obtained from ERR 26 and ERR 42 accessions provided broad benefits for cotton plants, improving the accumulation of N and P and improving plant growth and production. It is worth highlighting the significant contribution of the ERR 42 inoculant to the accumulation of calcium, which brings additional benefits to the formation and resistance of the fruit cell wall. This information is unprecedented and elevates the usage prospects of this inoculant for the biofertilization of cotton plants. However, further studies should be deepened to attest to its biostimulant advantages in cotton management.
Opinion Market Model: Stemming Far-Right Opinion Spread Using Positive Interventions
Authors: Calderon Pio , Ram Rohit , Rizoiu Marian-Andrei
Published: May 2024

Online extremism has severe societal consequences, including normalizing hate speech, user radicalization, and increased social divisions. Various mitigation strategies have been explored to address these consequences. One such strategy uses positive interventions: controlled signals that add attention to the opinion ecosystem to boost certain opinions. To evaluate the effectiveness of positive interventions, we introduce the Opinion Market Model (OMM), a two-tier online opinion ecosystem model that considers both inter-opinion interactions and the role of positive interventions. The size of the opinion attention market is modeled in the first tier using the multivariate discrete-time Hawkes process; in the second tier, opinions cooperate and compete for market share, given limited attention using the market share attraction model. We demonstrate the convergence of our proposed estimation scheme on a synthetic dataset. Next, we test OMM on two learning tasks, applying to two real-world datasets to predict attention market shares and uncover latent relationships between online items. The first dataset comprises Facebook and Twitter discussions containing moderate and far-right opinions about bushfires and climate change. The second dataset captures popular VEVO artists' YouTube and Twitter attention volumes. OMM outperforms the state-of-the-art predictive models on both datasets and captures latent cooperation-competition relations. We uncover (1) self- and cross-reinforcement between far-right and moderate opinions on the bushfires and (2) pairwise artist relations that correlate with real-world interactions such as collaborations and long-lasting feuds. Lastly, we use OMM as a testbed for positive interventions and show how media coverage modulates the spread of far-right opinions.
The Quiet Power of Social Media: Impact on Fish-Oil Purchases in Iceland during COVID-19
Authors: Carpinelli Camilla , Islind Anna Sigridur , Óskarsdóttir María
Published: May 2024

The rise of social media has revolutionized communication and the sharing of information and interests, with a significant impact on purchasing behavior. Consumers increasingly rely on social media for product recommendations and reviews, often finding themselves "accidentally influenced" by other users' posts and advice. This study examines the impact of social media in Iceland during the COVID-19 pandemic when there was a surge of posts giving dietary advice to prevent or treat the virus or its symptoms. One example is the rise and fall of fish oil advice. Using a large-scale dataset from one of the most popular supermarket chains in Iceland and netnography, we apply Data Science to analyze: sales data; Google search trends; Twitter posts from 2019 and 2020 to understand the impact of the online world on purchasing behavior in the offline world. Our results show the massive power of social media on people's purchasing behavior, particularly during a pandemic, and provide a comparison of consumer behavior before and during COVID-19.
Intermedia Agenda Setting during the 2016 and 2020 U.S. Presidential Elections
Authors: Chen Yijing , Liu Yaguang , Singh Lisa , Budak Ceren
Published: May 2024

Intermedia agenda setting (IAS) theory suggests that different news sources can influence each other's agenda. While this theory has been well-established in existing literature, whether it still holds in today's high-choice media environment, which includes news producers of different credibility and ideology dispositions, is an open question. Through two case studies--the 2016 and 2020 U.S. presidential elections--we show that media are still largely aligned, especially in broad topics they choose to cover, and that the level of alignment along the credibility dimension is comparable to that along the ideology dimension. Furthermore, we find that the coverage of the Republican candidate is better aligned across different media types than that of the Democratic candidate, and that media divergence has increased along both dimensions from 2016 to 2020. Finally, we demonstrate that high-credibility media still plays a dominant role in the IAS process, yet with a cautious warning of its declining IAS power for the Democratic candidate over the course of four years.
Clock against Chaos: Dynamic Assessment and Temporal Intervention in Reducing Misinformation Propagation
Authors: Ghosh Shreya , Mitra Prasenjit , Nakov Preslav
Published: May 2024

As social networks become the primary sources of information, the rise of misinformation poses a significant threat to the information ecosystem. Here, we address this challenge by proposing a dynamic system for real-time evaluation and assignment of misinformation scores to tweets, which can support the ongoing efforts to counteract the impact of misinformation public health, public opinion, and society. We use a unique combination of Temporal Graph Network (TGN) and Recurrent Neural Networks (RNNs) to capture both structural and temporal characteristics of misinformation propagation. We further use active learning to refine the understanding of misinformation, and a dual model system to ensure the accurate grading of tweets. Our system also incorporates a temporal embargo strategy based on belief scores, allowing for comprehensive assessment of information over time. We further outline a retraining strategy to keep the model current and robust in the dynamic misinformation landscape. The evaluation results across five social media misinformation datasets show promising accuracy in identifying false information and reducing propagation by a significant margin.
ArguSense: Argument-Centric Analysis of Online Discourse
Authors: Irani Arman , Faloutsos Michalis , Esterling Kevin
Published: May 2024

How can we model arguments and their dynamics in online forum discussions? The meteoric rise of online forums presents researchers across different disciplines with an unprecedented opportunity: we have access to texts containing discourse between groups of users generated in a voluntary and organic fashion. Most prior work so far has focused on classifying individual monological comments as either argumentative or not argumentative. However, few efforts quantify and describe the dialogical processes between users found in online forum discourse: the structure and content of interpersonal argumentation. Modeling dialogical discourse requires the ability to identify the presence of arguments, group them into clusters, and summarize the content and nature of clusters of arguments within a discussion thread in the forum. In this work, we develop ArguSense, a comprehensive and systematic framework for understanding arguments and debate in online forums. Our framework consists of methods for, among other things: (a) detecting argument topics in an unsupervised manner; (b) describing the structure of arguments within threads with powerful visualizations; and (c) quantifying the content and diversity of threads using argument similarity and clustering algorithms. We showcase our approach by analyzing the discussions of four communities on the Reddit platform over a span of 21 months. Specifically, we analyze the structure and content of threads related to GMOs in forums related to agriculture or farming to demonstrate the value of our framework.
A Semantic Interpreter for Social Media Handles
Authors: Islam Azwad Anjum , Finlayson Mark A.
Published: May 2024

A handle is a short string of characters that identifies a user or account in a social media platform and is unique within the scope of the platform. Though usually of limited length, a handle can often be the most information-dense string in a social media user profile, potentially containing clues to the user’s name, age, location, demographics, or group affiliations. Despite this, the handle has been frequently set aside in work related to inferring user information from their social media profiles. We present a technique for semantic parsing of handles, which seeks to extract relevant information from the handle string. The technique is rule-based and relies on a set of tokenization rules and a variety of external databases (e.g., of names or places) to provide potential interpretations of handles in terms of names, locations, dates, indices, years, ages, positive/negative sentiments, and acronyms. We evaluate an implementation of the technique for English against existing corpora as well as manually evaluate parses of randomly sampled handles, showing that our method achieves good results in both tokenizing the handles (84.9% chance that the correct tokenization is in top three parses while 97% chance that one of the top three parses are at least reasonable) and providing overall “optimistic” interpretation performance of 90.1% accuracy and 0.89 F1. We also evaluate performance on each of the semantic aspects we interpret (name, location, index, year, age, sentiment, acronym). The technique not only allows us to extract additional information about a
user from their handle but also allows us to measure trends in how handles are constructed on specific social media websites. We find that 59% of the handles in our data contain at least part of a person’s name, and over 69% of the handles are indicative of the user’s gender identity in some way. While our implementation targets English, it can be easily adapted to other languages given the appropriate databases. We release both our code and annotated evaluation data to aid other researchers in validating or extending our work.
Gender Gaps in Online Social Connectivity, Promotion and Relocation Reports on LinkedIn
Authors: Kalhor Ghazal , Gardner Hannah , Weber Ingmar , Kashyap Ridhi
Published: May 2024

Online professional social networking platforms provide opportunities to expand networks strategically for job opportunities and career advancement. A large body of research shows that women’s offline networks are less advantageous than men’s. How online platforms such as LinkedIn may reflect or reproduce gendered networking behaviours, or how online social connectivity may affect outcomes differentially by gender is not well understood. This paper analyses aggregate, anonymised data from almost 10 million LinkedIn users in the UK and US information technology (IT) sector collected from the site’s advertising platform to explore how being connected to Big Tech companies (‘social connectivity’) varies by gender, and how gender, age, seniority and social connectivity shape the propensity to report job promotions or relocations. Consistent with previous studies, we find there are fewer women compared to men on LinkedIn in IT. Furthermore, female users are less likely to be connected to Big Tech companies than men. However, when we further analyse recent promotion or relocation reports, we find women are more likely than men to have reported a recent promotion at work, suggesting high-achieving women may be self-selecting onto LinkedIn. Even among this positively selected group, though, we find men are more likely to report a recent relocation. Social connectivity emerges as a significant predictor of promotion and relocation reports, with an interaction effect between gender and social connectivity indicating the payoffs to social connectivity for promotion and relocation reports are larger for women. This suggests that online networking has the potential for larger impacts for women, who experience greater disadvantage in traditional networking contexts, and calls for further research to understand differential impacts of online networking for socially disadvantaged groups.
A Cross-Platform Topic Analysis of the Nazi Narrative on Twitter and Telegram during the 2022 Russian Invasion of Ukraine
Authors: Kloo Ian , Cruickshank Iain J. , Carley Kathleen M.
Published: May 2024

To influence the information landscape preceding and during the military invasion of Ukraine in February 2022, Russia initiated a disinformation campaign portraying Ukraine as a Nazi state. This study aims to compare discussions related to this campaign on Twitter and Telegram. The analysis reveals that the Nazis and Ukraine narrative was constant on Twitter but only emerged on Telegram after the invasion in channels that had previously focused on a broader set of conspiracy theories. Beyond the examination of Russian disinformation in this case study, the paper introduces an innovative methodology for constructing topic networks from social media data. This approach expands upon traditional topic modeling by incorporating the network properties of social media data to establish directed networks that characterize the interplay between conversation topics. Through this methodology, we gain the ability to observe topical evolutions, providing fresh insights into the disinformation campaign and its efficacy in shaping discussions around the Russian invasion on social media.
Topic Shifts as a Proxy for Assessing Politicization in Social Media
Authors: Locatelli Marcelo Sartori , Calais Pedro , Miranda Matheus Prado , Junho João Pedro , Muniz Tomas Lacerda , Meira Jr. Wagner , Almeida Virgilio
Published: May 2024

Politicization is a social phenomenon studied by political science characterized by the extent to which ideas and facts are given a political tone. A range of topics, such as climate change, religion and vaccines has been subject to increasing politicization in the media and social media platforms. In this work, we propose a computational method for assessing politicization in online conversations based on topic shifts, i.e., the degree to which people switch topics in online conversations. The intuition is that topic shifts from a non-political topic to politics are a direct measure of politicization – making something political, and that the more people switch conversations to politics, the more they perceive politics as playing a vital role in their daily lives. A fundamental challenge that must be addressed when one studies politicization in social media is that, a priori, any topic may be politicized. Hence, any keyword-based method or even machine learning approaches that rely on topic labels to classify topics are expensive to run and potentially ineffective. Instead, we learn from a seed of political keywords and use Positive-Unlabeled (PU) Learning to detect political comments in reaction to non-political news articles posted on Twitter, YouTube, and TikTok during the 2022 Brazilian presidential elections. Our findings indicate that all platforms show evidence of politicization as discussion around topics adjacent to politics such as economy, crime and drugs tend to shift to politics. Even the least politicized topics had the rate in which their topics shift to politics increased in the lead up to the elections and after other political events in Brazil – an evidence of politicization. The code is available at https://github.com/marceloslo/Topic-Shifts-as-a-Proxy-for-Assessing-Politicization-in-Social-Media.
Keeping Up with the Winner! Targeted Advertisement to Communities in Social Networks
Authors: Mallick Shailaja , Doshi Vishwaraj , Eun Do Young
Published: May 2024

When a new product enters a market already dominated by an existing product, will it survive along with this dominant product? Most of the existing works have shown the coexistence of two competing products spreading/being adopted on overlaid graphs with same set of users. However, when it comes to the survival of a weaker product on the same graph, it has been established that the stronger one dominates the market and wipes out the other. This paper makes a step towards narrowing this gap so that a new/weaker product can also survive along with its competitor with a positive market share. Specifically, we identify a locally optimal set of users to induce a community that is targeted with advertisement by the product launching company under a given budget constraint. To this end, we model the system as competing Susceptible-Infected-Susceptible (SIS) epidemics and employ perturbation techniques to quantify and attain a positive market share in a cost-efficient manner. Our extensive simulation results with real-world graph dataset show that with our choice of target users, a new product can establish itself with positive market share, which otherwise would be dominated and eventually wiped out of the competitive market under the same budget constraint.
Don’t Break the Chain: Measuring Message Forwarding on WhatsApp
Authors: Melo Philipe de Freitas , Hoseini Mohamad , Zannettou Savvas , Benevenuto Fabrício
Published: May 2024

WhatsApp has evolved into a popular communication tool, facilitating the exchange of billions of multimedia messages globally. With its large public groups and forwarding features, the platform has enabled messages to go viral, rapidly disseminating across the WhatsApp network. This has also brought WhatsApp to a central position in spreading misinformation campaigns, prompting the company to implement measures to counter bulk message dissemination, such as limiting simultaneous forwards and flagging viral content.
Despite these measures, there remains a gap in our understanding of how forwarded messages function within this ecosystem and the effectiveness of the restrictions in containing the spread of viral content. In this study, we analyze approximately 10 million messages from 1,101 public WhatsApp groups dedicated to political discussion in Brazil, focusing on forwarded content. We investigate the structure of message forwarding, assess the reach of Forwarded Many Times (FTM) labeling mechanism, and evaluate the platform's ability to detect and flag duplicated media.
Our findings reveal that forwarded messages constitute a substantial portion of the content shared in public WhatsApp groups. Moreover, we discover that the measures implemented by WhatsApp to restrict the dissemination of such messages can be easily circumvented, allowing users to intentionally bypass the architecture of the system and share media beyond the imposed limits. Notably, we identify that 59% of duplicated content flagged as FMT by WhatsApp does not receive the corresponding flag and find evidences of misinformation circulating virally in those groups.
This research provides valuable insights into the dynamics of forwarded messages on WhatsApp and highlights the need for more effective strategies to combat the spread of viral content within the platform.
Online Social Behavior Enhanced Detection of Political Stances in Tweets
Authors: Peng Xingyu , Zhou Zhenkun , Zhang Chong , Xu Ke
Published: May 2024

Public opinion plays a pivotal role in politics, influencing political leaders' decisions, shaping election outcomes, and impacting policy-making processes. In today's digital age, the abundance of political discourse available on social media platforms has become an invaluable resource for analyzing public opinion. This paper focuses on the task of detecting political stances in the context of the 2020 US presidential election. To facilitate this research, we curate a substantial dataset sourced from Twitter, annotated using hashtags as indicators of political polarity. In our approach, we construct a bipartite graph that explicitly models user-tweet interactions, which provides a comprehensive contextual understanding of the election. To effectively leverage the wealth of user behavioral information encoded in this graph, we adopt graph convolution and introduce a novel skip aggregation mechanism. This mechanism enables tweet nodes to aggregate information from their second-order neighbors, which are also tweet nodes due to the graph's bipartite nature. Our experimental results demonstrate that our proposed model outperforms a range of competitive baseline models. Furthermore, our in-depth analyses highlight the importance of user behavioral information and the effectiveness of skip aggregation.
Curious Rhythms: Temporal Regularities of Wikipedia Consumption
Authors: Piccardi Tiziano , Gerlach Martin , West Robert
Published: May 2024

Wikipedia, in its role as the world's largest encyclopedia, serves a broad range of information needs. Although previous studies have noted that Wikipedia users' information needs vary throughout the day, there is to date no large-scale, quantitative study of the underlying dynamics.
The present paper fills this gap by investigating temporal regularities in daily consumption patterns in a large-scale analysis of billions of timezone-corrected page requests mined from English Wikipedia's server logs, with the goal of investigating how context and time relate to the kind of information consumed. First, we show that even after removing the global pattern of day-night alternation, the consumption habits of individual articles maintain strong diurnal regularities. Then, we characterize the prototypical shapes of consumption patterns, finding a particularly strong distinction between articles preferred during the evening/night and articles preferred during working hours. Finally, we investigate topical and contextual correlates of Wikipedia articles' access rhythms, finding that article topic, reader country, and access device (mobile vs. desktop) are all important predictors of daily attention patterns.
These findings shed new light on how humans seek information on the Web by focusing on Wikipedia as one of the largest open platforms for knowledge and learning, emphasizing Wikipedia's role as a rich knowledge base that fulfills information needs spread throughout the day, with implications for understanding information seeking across the globe and for designing appropriate information systems.
How COVID-19 Has Impacted the Anti-vaccine Discourse: A Large-Scale Twitter Study Spanning Pre-COVID and Post-COVID Era
Authors: Poddar Soham , Mukherjee Rajdeep , Khatuya Subhendu , Ganguly Niloy , Ghosh Saptarshi
Published: May 2024

The debate around vaccines has been going on for decades, but the COVID-19 pandemic showed how crucial it is to understand and mitigate anti-vaccine sentiments. While the pandemic may be over, it is still important to understand how the pandemic affected the anti-vaccine discourse, and whether the arguments against non-COVID vaccines have also changed due to the pandemic. This study attempts to answer these questions through a large-scale study of anti-vaccine posts on Twitter. Almost all prior works that utilized social media to understand anti-vaccine opinions considered only the three broad stances of Anti-Vax, Pro-Vax, and Neutral. There has not been any effort to identify the specific reasons/concerns behind the anti-vax sentiments (e.g., side-effects, conspiracy theories, political reasons) on social media at scale. In this work, we propose two novel methods for classifing tweets into 11 different anti-vax concerns -- a discriminative approach (entailment-based) and a generative approach (based on instruction tuning of LLMs) -- which outperform several strong baselines. We then apply this classifier on anti-vaccine tweets posted over a 5-year period (Jan 2018 - Jan 2023) to understand how the COVID-19 pandemic has impacted the anti-vaccine concerns among the masses. We find that the pandemic has made the anti-vaccine discourse far more complex than in the pre-COVID times, and increased the variety of concerns being voiced. Alarmingly, we find that concerns about COVID vaccines are now being projected onto the non-COVID vaccines, thus making more people hesitant in taking them in post-COVID times.
The Diffusion of Causal Language in Social Networks
Authors: Shi Zhuoyu , Morstatter Fred
Published: May 2024

Causal reasoning plays a central role in human cognition. It facilitates the ability to infer, predict, and manipulate outcomes within the environment, which in turn lays the foundation for a uniquely adaptive decision-making framework that is crucial in navigating complex problem-solving contexts. With the pervasive influence of social media platforms, these online social networks have become critical for disseminating information, shaping public beliefs, and influencing daily life. However, no study has examined the propagation of causal language within social networks. In this work, we analyze the dispersion of messages containing causal language against those without, within the milieu of a large online social network. With the entirety of messages over one complete day on Twitter along with two additional days for validation, and with our validated ensemble method for identifying causal language, our findings reveal that messages with causal language exhibit a more extensive reach than those without. Furthermore, our counterfactual analysis demonstrates that the effect of causal language on information diffusion is truly causal. Moreover, our findings indicate that messages incorporating causal language manifest a higher ability to spread to out-groups compared to those without. These novel insights reveal the unique diffusion pattern of causal language within social networks, and suggest a potential to mitigate the echo chamber effect, while causal language could serve as a bridge for diverse perspectives.
Submodular Optimization beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertising
Authors: Tang Shaojie , Yuan Jing
Published: May 2024

Social advertising, also known as social promotion, is a method of promoting products or ideas through the use of influential individuals, known as ``seeds,'' on online social networks. Advertisers and platforms are the main players in this ecosystem, with platforms selling viral engagements, such as ``likes,'' to advertisers by inserting ads into the feeds of seeds. Seeds are given monetary incentives by the platform in exchange for their participation in the campaign, and when a follower of a seed engages with an ad, the platform receives payment from the advertiser. Specifically, at the beginning of a campaign, the advertiser submits a budget to the platform and this budget can be used for two purposes: recruiting seeds and paying for the viral engagements generated by the seeds. Note that the first part of payment goes to the seeds and the latter one is the actual revenue collected by the platform. The challenge for the platform is to select a group of seeds that will generate the most revenue within the budget constraints set by the advertiser. This problem is challenging as the objective function can be non-monotone and may take on negative values. This makes traditional methods of submodular optimization and influence maximization inapplicable. We study this problem under both non-adaptive and adaptive settings, and propose effective solutions for each scenario.
Detection and Categorization of Needs during Crises Based on Twitter Data
Authors: Yang Pingjing , Dinh Ly , Stratton Alex , Diesner Jana
Published: May 2024

The Ukraine-Russia conflict has brought sizable detrimental impact to the global energy, food, finance, and manufacturing industries, as well to many affected people. In this paper, we use Twitter (now X) to automatically identify who needs what from text data and how the types of needs that we categorized and standardize evolved throughout this conflict. Our findings suggest that the Ukraine expresses a need for weapons, Russia for land, Europe for gas, and America for leadership. The majority of needs expressed on Twitter during this conflict are related to the categories transportation, military, health & medical, financial and money, energy, and essential items (food, water, shelter, non-food items). Stated needs changed as the conflict escalated or fell into stalemate. Needs also varied depending on the tweet's location, with tweets from Ukraine's neighboring countries being related to food and medicine, while tweets from non-neighboring countries stated needs for clothing and tents. Tweets written in Ukrainian and Russian shared similar need terms, such as medicines and kits, compared to English tweets, which expressed needs such as ammunition and humanitarian aid. Our comparison of needs across four different disaster events, namely this conflict, an earthquake, a major hurricane, and the COVID-19 pandemic, showed how needs differ depending on the nature of the crisis and how domain-adjustment of needs categories is necessary. We contribute to the crisis informatics literature by (1) validating a methodology for using tweets to study the demand and supply of things that different stakeholders need during crisis events and (2) testing, comparing, and improving the fit of widely used need classification schemas for studying crisis from different domains.
Evaluating and Improving Value Judgments in AI: A Scenario-Based Study on Large Language Models’ Depiction of Social Conventions
Authors: You Jaeyoun , Suh Bongwon
Published: May 2024

The adoption of generative AI technologies is swiftly expanding. Services employing both linguistic and multimodal models are evolving, offering users increasingly precise responses. Consequently, human reliance on these technologies is expected to grow rapidly. With the premise that people will be impacted by the output of AI, we explored approaches to help AI output produce better results. Initially, we evaluated how contemporary AI services competitively meet user needs, then examined society's depiction as mirrored by Large Language Models (LLMs). We did a query experiment, querying about social conventions in various countries and eliciting a one-word response. We compared the LLMs' value judgments with public data and suggested a model of decision-making in value-conflicting scenarios which could be adopted for future machine value judgments. This paper advocates for a practical approach to using AI as a tool for investigating other remote worlds. This research has significance in implicitly rejecting the notion of AI making value judgments and instead arguing a more critical perspective on the environment that defers judgemental capabilities to individuals. We anticipate this study will empower anyone, regardless of their capacity, to receive safe and accurate value judgment-based outputs effectively.
DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks
Authors: Zhang Chong , Zhou Zhenkun , Peng Xingyu , Xu Ke
Published: May 2024

Given the development and abundance of social media, studying the stance of social media users is a challenging and pressing issue. Social media users express their stance by posting tweets and retweeting. Therefore, the homogeneous relationship between users and the heterogeneous relationship between users and tweets are relevant for the stance detection task. Recently, graph neural networks (GNNs) have developed rapidly and have been applied to social media research. In this paper, we crawl a large-scale dataset of the 2020 US presidential election and automatically label all users by manually tagged hashtags. Subsequently, we propose a bipartite graph neural network model, DoubleH, which aims to better utilize homogeneous and heterogeneous information in user stance detection tasks. Specifically, we first construct a bipartite graph based on posting and retweeting relations for two kinds of nodes, including users and tweets. We then iteratively update the node's representation by extracting and separately processing heterogeneous and homogeneous information in the node's neighbors. Finally, the representations of user nodes are used for user stance classification. Experimental results show that DoubleH outperforms the state-of-the-art methods on popular benchmarks. Further analysis illustrates the model's utilization of information and demonstrates stability and efficiency at different numbers of layers.
Language-Agnostic Modeling of Wikipedia Articles for Content Quality Assessment across Languages
Authors: Das Paramita , Johnson Isaac , Saez-Trumper Diego , Aragón Pablo
Published: May 2024

Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles.

State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use.
The Koo Dataset: An Indian Microblogging Platform with Global Ambitions
Authors: Mekacher Amin , Falkenberg Max , Baronchelli Andrea
Published: May 2024

Increasingly, alternative platforms are playing a key role in the social media ecosystem. Koo, a microblogging platform based in India, has emerged as a major new social network hosting high profile politicians from several countries (India, Brazil, Nigeria) and many internationally renowned celebrities. This paper presents the largest publicly available Koo dataset, spanning from the platform’s founding in early 2020 to September 2023, providing detailed metadata for 72M posts, 75M comments, 40M shares, 284M likes and 1.4M user profiles. Along with the release of the dataset, we provide an overview of the platform including a discussion of the news ecosystem on the platform, hashtag usage, and user engagement. Our results highlight the pivotal role that new platforms play in shaping online communities in emerging economies and the Global South, connecting local politicians and public figures with their followers. With Koo’s ambition to become the town hall for diverse non-English speaking communities, our dataset offers new opportunities for studying social media beyond a Western context.
Tube2Vec: Social and Semantic Embeddings of YouTube Channels
Authors: Boesinger Léopaul , Horta Ribeiro Manoel , Veselovsky Veniamin , West Robert
Published: May 2024

Research using YouTube data often explores social and semantic dimensions of channels and videos. Typically, analyses rely on laborious manual annotation of content and content creators, often found by low-recall methods such as keyword search. Here, we explore an alternative approach, Tube2Vec, using latent representations (embeddings) obtained via machine learning. Using a large dataset of YouTube links shared on Reddit; we create embeddings that capture social sharing behavior, video metadata (title, description, etc.), and YouTube's video recommendations. We evaluate these embeddings using crowdsourcing and existing datasets, finding that recommendation embeddings excel at capturing both social and semantic dimensions, although social-sharing embeddings better correlate with existing partisan scores. We share embeddings capturing the social and semantic dimensions of 44,000 YouTube channels for the benefit of future research on YouTube. https://github.com/epfl-dlab/youtube-embeddings.
RNA extraction for Karlodinium veneficum PLY720 v1
Authors: Lewis Will
Published: May 2024

A methodology for cell harvesting and RNA extraction from Karlodinium veneficum for the purposes of RNA-seq.
Variant anatomy of the internal iliac vein and its applications
Authors: Rumyantsev Valery N. , Gaivoronsky Ivan V. , Surov Dmitry A. , Nichiporuk Gennady I. , Balyura Oleg V.
Published: May 2024

The article analyzed data from relevant literature that focused on anatomical variations of the internal iliac vein (IIV), its structure, and morphometric characteristics.
P.A. LePage et al. (1991), D. Venieratos et al. (2012), and P. Kanjanasilp et al. (2019) have previously evaluated anatomical variations of the IIV using postvital materials. M. Shin et al. (2015) extensively analyzed the architectonics of the main trunk of the IIV according to vestibular methods (n=2488) and identified eight variants. This approach formed the basis of a similar work (n=1071) by A. Hekimoglu and O. Ergun (2021). D. Sat-Muñoz et al. (2020) and M.G. Shkvarko et al. (2021) also conducted a morphometric study of IIVs. D. Sat-Muñoz et al. categorized variants of IIV formation into three types, according to the classification of R. Gregoire (2007), based on the relationship of the IIV with its artery. According to D. Kachlik et al. (2010), in approximately 50% of cases, the root of the IIV is the internal genital vein, which penetrates the pelvis through the subclavian orifice; in 30%, the anterior and posterior trunks can be identified in the architectonics of the IIV by analogy with the internal iliac artery, which are the roots; in 20%, the IIV trunk is formed by many small tributaries, and roots and trunks could not be identified.
Our data revealed that the typical IIV variant is the one in which its roots and tributaries (except for the umbilical vein) correspond to the branches of the artery of the same name, and a typical variant of IIV architectonics should be considered the fusion of the anterior and posterior trunks, with the internal genital vein and superior gluteal vein as the roots, respectively. All visceral tributaries flow into the anterior trunk, and nearly all extramural tributaries accompanying branches of the internal iliac artery flow into the posterior trunk.
The majority of authors studied the architectonics of the main trunk of the IIV without the systematization of its tributaries. Based on the review of available literature, no modern clinically oriented classification of the variability of IIV architectonics has been established, satisfying the needs of modern surgery during interventions on the pelvic organs. Knowledge of the IIV anatomical variants and its tributaries is necessary when performing surgical interventions in the small pelvis, particularly during evisceration, to prevent blood loss.
This article outlined promising directions in the study of the IIV and its tributaries, namely, methods of three-dimensional modeling, including the use of augmented reality technology, at various stages of surgical treatment of locally advanced pelvic tumors.
From Alienation to Revolution: Female Characters as Decolonial Tools in J.L. Torres’s The Accidental Native
Authors: Martínez Reyes Consuelo
Published: May 2024

In J.L. Torres’s The Accidental Native (2013), Rennie moves back to his native Puerto Rico after being raised in the United States. There, he meets his true biological mother, as well as his future (female) partner. Rennie’s unawareness of his mother’s existence and Puerto Rico’s colonial situation, as well as his partner’s illness, interweave maternal and romantic love with the Island. Furthermore, their ties pre-configure the birth of Rennie’s decolonial consciousness. This analysis dialogues with representations of the United States as an oppressive patriarchal figure to the Island, its inept child, in order to incorporate and contrast the role of the mother, as well as other affective figures, within the long tradition of genealogical metaphors in the Island’s literature. A decolonial interpretation of J.L. Torres’ work proposes a familial configuration in which affective forces originating from female characters function as a tool of epistemic liberation against imperialist, colonizing, patriarchal powers, thus proposing theoretical ties between affective and decolonial studies.
Current trends in the methods of identification in forensic dentistry: a review of literature
Authors: A. Zahrani Ahmed
Published: May 2024

Dental evidence in forensic investigation has been in practice for many years. Forensic dentistry or forensic odontology predominantly involves the identification of human remains, by comparing the dental evidence of deceased persons recovered from the scene of an accident or a crime (post-mortem dental data) with the ante-mortem dental records. The use of dental identification lies in the fact that no two oral cavities are alike, and the teeth are unique for everyone. In addition, dental structures are not only the hardest and most protected structures in the body, but are also the most resistant to degradation due to environmental impact. Identification of a deceased individual using forensic odontology is possible with a higher degree certainty, as it is possible to apply multiple tools such as dental imaging, tongue print, lip print, rugoscopy, tooth signature, ameloglyphics, deoxyribonucleic acid (DNA) analysis, bite-marks, dentures and prosthesis, facial reconstruction, virtopsy and software applications. Since the role of a forensic dentist extends even beyond identification, especially in the absence of ante-mortem dental records for comparison, teeth become a determinant factor in identification, and determination of age, gender and ethnicity. Although several methods and techniques of identification have been described in forensic dentistry, there is no comprehensive resource that describes them all in detail. Therefore, this review paper aims to analyze and discuss the available methods of identification in forensic dentistry, their merits and limitations, and how they might be of help to the forensic dentists and pathologists.
Infective keratitis: a concise overview of the clinical characteristics and treatment of keratitis caused by microbial agents
Authors: Chandra Mahesh , Singh Jitendra , Jha Ayush
Published: May 2024

The following document provides an overview of microbial keratitis, a well-known sight-threatening inflammation of the cornea that progresses to the corneal ulcer. Sometimes, such infection is difficult to identify and cure due to the involvement of multiple pathogens implicated in the specific disorders because of similar symptoms and immunological responses. Instead of fungi and protozoa, viruses and bacteria are the most prevalent pathogens that cause microbial keratitis. A virus contains protein-encased genetic material and may infect any living creature, including bacteria and fungi, by replicating inside the host's cell and infecting neighbouring cells. Bacteria are complicated pathogens that may thrive in any media and cause harm to host cells, often through the production of toxins. Fungi are far more difficult; they spread quickly and can cause harm to several organs at the same time if the immune system is compromised. Protozas are found freely in the environment and once invade the cornea, divide quickly and become difficult to identify as well as treat, because of their involvement or in conjunction with polymicrobials. These microbes show common symptoms after invading the cornea although; their common diagnostic procedures show different results to trace out their existence in the tissue. Up to some extent, specific treatment can cure the disease with certain conditions according to a load of microbes, therefore visual status gets hampered, otherwise total loss of the eye takes place due to the endophthalmitis.
Cosmetology and Toxicology for B.Pharm / B.Tech Cosmetics Students
Authors: Mandan Ms. Savita , Gorle Dr. Ashish , Nerkar Dr. Pankaj
Published: May 2024

The world of cosmetology is a dynamic and ever-evolving field, blending artistry and science to enhance beauty and boost confidence. It is with great excitement and passion that I present this book, a comprehensive guide to the fascinating and intricate world of cosmetology.

The pages that follow are designed to serve as both an educational resource and a practical guide. From the fundamentals of skin care, hair styling, and makeup application, to the latest trends and techniques, this book covers a broad spectrum of topics essential for aspiring cosmetologists. Each chapter is meticulously crafted to offer detailed instructions, expert tips, and insightful anecdotes from experienced professionals.

In writing this book, I have drawn upon my own experiences, extensive research, and the wisdom of industry veterans. It is my hope that this compilation not only educates but also ignites a passion for lifelong learning and innovation in the field of cosmetology.

To the readers, whether you are a student, a practicing cosmetologist, or someone with a keen interest in beauty, I hope this book serves as a useful and inspiring tool. May it help you develop your skills, expand your knowledge, and achieve your goals in the captivating world of cosmetology.