bluuebunny/crossref_metadata_2025 huggingface dataset:
Language adaptation of ethnic Russians in the Western Ukrainian area
Authors:   Institute of the Ukrainian Language of the National Academy of Sciences of Ukraine, Tsar Ivanna
Published: June 2023

The article characterizes the language adaptation of ethnic Russians in the Western Ukrainian area on the example of language biographies of three ethnic Russians living in the same village in the Lviv Region. Changes in language and national identity, lan­guage behavior and the language itself of the informants under the influence of a long stay in the Ukrainian-speaking environment are outlined. The attitude of the informants towards the Ukrainian and Russian languages and the state language policy has been clarified. Their vision of the future language situation in Ukraine is characterized. External and internal factors that influenced the language adaptation of the informants have been identified, among which the language environment and political beliefs of the interviewees become the most important. Language adaptation of each of the interviewees takes place differently, although they live in a Ukrainian-speaking environment for approximately the same period. Du­ring adaptation, respondents did not face discrimination based on language or nationality. All interviewees emphasized the benevolent attitude of the locals towards them and did not witness language conflicts during their stay in the Ukrainian-speaking environment. Language adaptation of the informants takes place simultaneously with their socio­cultural adaptation. All interviewees have adopted local customs and traditions. Some speakers even have changed their political beliefs. Under the influence of the dominant Ukrainian language in this environment, significant changes have been in the language life of only those informants who wanted it themselves. The results of the informant’s language adaptation have influenced the future language and national identification of their children and grandchildren. Keywords: language biography, language adaptation, language behavior, language identity, language environment.
CFD modeling of subsonic and sonic methane gas release and dispersion
Authors: Tugyi Levente , Siménfalvi Zoltán , Szepesi Gábor L. , Kecskés Csaba , Kerekes Zoltán , Sári Tamás
Published: June 2023

In the event of a flammable liquid, gas, or vapor release the first step is to identify the type of outflow, which can fall into two categories sonic or subsonic. The two types of outflows carry different flow characteristics, which effect on the extent of the potentially explosive areas. In case of subsonic outflow, a short jet is formed without turbulent flow conditions at low velocity, which appears more concentrated around the source of release. With sonic outflow, a high velocity jet is formed with turbulent flow properties, which can extend further away from the source of release. The simulations examine the lower explosion limit of the flammable medium around the vessel where LEL20% or LEL40%. In addition, high temperature methane gas release was also presented.
Az akut gastroduodenalis fekélyvérzés gyógyszeres és endoszkópos kezelésének újabb szempontjai
Authors: Rácz István
Published: June 2023

Az akut gastroduodenalis fekélyvérzés kórházi ellátást és több szakma
összefogását igénylő életveszélyes állapot. A kezelési program felöleli a
keringést stabilizáló sürgősségi ellátást, a szakszerű transzfundálást, a
savszekréció-gátló terápiát, az endoszkópos diagnosztikát és kezelést, továbbá
esetenként az invazív radiológiai és a sebészeti beavatkozást is. Az újabb
ajánlások csupán megfontolásra ajánlják, de nem javasolják a preendoszkópos nagy
dózisú parenteralis protonpumpagátló kezelést. A sürgős (≤12 óra) endoszkópia
klinikailag nem előnyösebb a korai (≤24 óra) endoszkópiánál. Nagy újravérzési
kockázatú, a 2 cm-es átmérőt elérő fibroticus alapú és 2 mm-nél nagyobb
ércsonkos vérző fekélyek „over-the-scope clip” kezelése már első vonalbeli
kezelésként is ajánlott. Új terápiás lehetőség az endoszkópos vérzéscsillapítás
utáni, intermittálóan adagolt parenteralis protonpumpagátló kezelés. A tervezett
ellenőrző endoszkópiák programszerűen nem ajánlottak, de magas újravérzési
kockázatú fekélyvérzőkben egyéni mérlegelés után tanácsosak. Gastroduodenalis
fekélyvérzőkben a primer cardiovascularis profilaxis célú, kis dózisú aszpirin
elhagyása indokolt, de a szekunder megelőzésként végzett aszpirinkezelés
folytatható. Orv Hetil. 2023; 164(23): 883–890.
Exposure to Marginally Abusive Content on Twitter
Authors: Bandy Jack , Lazovich Tomo
Published: June 2023

Social media platforms can help people find connection and entertainment, but they can also show potentially abusive content such as insults and targeted cursing. While platforms do remove some abusive content for rule violation, some is considered "margin content" that does not violate any rules and thus stays on the platform. This paper presents a focused analysis of exposure to such content on Twitter, asking (RQ1) how exposure to marginally abusive content varies across Twitter users, and (RQ2) how algorithmically-ranked timelines impact exposure to marginally abusive content. Based on one month of impression data from November 2021, descriptive analyses (RQ1) show significant variation in exposure, with more active users experiencing higher rates and higher volumes of marginal impressions. Experimental analyses (RQ2) show that users with algorithmically-ranked timelines experience slightly lower rates of marginal impressions. However, they tend to register more total impression activity and thus experience a higher cumulative volume of marginal impressions. The paper concludes by discussing implications of the observed concentration, the multifaceted impact of algorithmically-ranked timelines, and potential directions for future work.
Followback Clusters, Satellite Audiences, and Bridge Nodes: Coengagement Networks for the 2020 US Election
Authors: Beers Andrew , Schafer Joseph S. , Kennedy Ian , Wack Morgan , Spiro Emma S. , Starbird Kate
Published: June 2023

The 2020 United States (US) presidential election was — and has continued to be — the focus of pervasive and persistent mis- and disinformation spreading through our media ecosystems, including social media. This event has driven the collection and analysis of large, directed social network datasets, but such datasets can resist intuitive understanding. In such large datasets, the overwhelming number of nodes and edges present in typical representations create visual artifacts, such as densely overlapping edges and tightly-packed formations of low-degree nodes, which obscure many features of more practical interest. We apply a method, coengagement transformations, to convert such networks of social data into tractable images. Intuitively, this approach allows for parameterized network visualizations that make shared audiences of engaged viewers salient to viewers. Using the interpretative capabilities of this method, we perform an extensive case study of the 2020 United States presidential election on Twitter, contributing an empirical analysis of coengagement. By creating and contrasting different networks at different parameter sets, we define and characterize several structures in this discourse network, including bridging accounts, satellite audiences, and followback communities. We discuss the importance and implications of these empirical network features in this context. In addition, we release open-source code for creating coengagement networks from Twitter and other structured interaction data.
What Are You Anxious About? Examining Subjects of Anxiety during the COVID-19 Pandemic
Authors: Chen Lucia L. , Wilson Steven R. , Lohmann Sophie , Negraia Daniela V.
Published: June 2023

COVID-19 poses disproportionate mental health consequences to the public during different phases of the pandemic. We use a computational approach to capture the specific aspects that trigger the public's anxiety about the pandemic and investigate how these aspects change over time. First, we identified nine subjects of anxiety (SOAs) in a sample of Reddit posts (N=86) from r/COVID19\_support using the thematic analysis approach. Then, we quantified Reddit users' anxiety by training algorithms on a manually annotated sample (N=793) to annotate the SOAs in a larger chronological sample (N=6,535). The nine SOAs align with items in various recently developed pandemic anxiety measurement scales. We observed that Reddit users' concerns about health risks remained high in the first eight months since the pandemic started. These concerns diminished dramatically despite the surge of cases occurring later. In general, users' language disclosing the SOAs became less intense as the pandemic progressed. However, worries about mental health and the future steadily increased throughout the period covered in this study. People also tended to use more intense language to describe mental health concerns than health risk or death concerns. Our results suggest that the public's mental health condition does not necessarily improve despite COVID-19 as a health threat gradually weakening due to appropriate countermeasures. Our system lays the groundwork for population health and epidemiology scholars to examine aspects that provoke pandemic anxiety in a timely fashion.
We Are in This Together: Quantifying Community Subjective Wellbeing and Resilience
Authors: Dong MeiXing , Sun Ruixuan , Biester Laura , Mihalcea Rada
Published: June 2023

The COVID-19 pandemic disrupted everyone's life across the world. In this work, we characterize the subjective wellbeing patterns of 112 cities across the United States during the pandemic prior to vaccine availability, as exhibited in subreddits corresponding to the cities. We quantify subjective wellbeing using positive and negative affect. We then measure the pandemic's impact by comparing a community's observed wellbeing with its expected wellbeing, as forecasted by time series models derived from prior to the pandemic. We show that general community traits reflected in language can be predictive of community resilience. We predict how the pandemic would impact the wellbeing of each community based on linguistic and interaction features from normal times before the pandemic. We find that communities with interaction characteristics corresponding to more closely connected users and higher engagement were less likely to be significantly impacted. Notably, we find that communities that talked more about social ties normally experienced in-person, such as friends, family, and affiliations, were actually more likely to be impacted. Additionally, we use the same features to also predict how quickly each community would recover after the initial onset of the pandemic. We similarly find that communities that talked more about family, affiliations, and identifying as part of a group had a slower recovery.
Scope of Pre-trained Language Models for Detecting Conflicting Health Information
Authors: Gatto Joseph , Basak Madhusudan , Preum Sarah Masud
Published: June 2023

An increasing number of people now rely on online platforms to meet their health information needs. Thus identifying inconsistent or conflicting textual health information has become a safety-critical task. Health advice data poses a unique challenge where information that is accurate in the context of one diagnosis can be conflicting in the context of another. For example, people suffering from diabetes and hypertension often receive conflicting health advice on diet. This motivates the need for technologies which can provide contextualized, user-specific health advice. A crucial step towards contextualized advice is the ability to compare health advice statements and detect if and how they are conflicting. This is the task of health conflict detection (HCD). Given two pieces of health advice, the goal of HCD is to detect and categorize the type of conflict. It is a challenging task, as (i) automatically identifying and categorizing conflicts requires a deeper understanding of the semantics of the text, and (ii) the amount of available data is quite limited.

In this study, we are the first to explore HCD in the context of pre-trained language models. We find that DeBERTa-v3 performs best with a mean F1 score of 0.68 across all experiments. We additionally investigate the challenges posed by different conflict types and how synthetic data improves a model's understanding of conflict-specific semantics. Finally, we highlight the difficulty in collecting real health conflicts and propose a human-in-the-loop synthetic data augmentation approach to expand existing HCD datasets. Our HCD training dataset is over 2x bigger than the existing HCD dataset and is made publicly available on Github.
A Data Fusion Framework for Multi-Domain Morality Learning
Authors: Guo Siyi , Mokhberian Negar , Lerman Kristina
Published: June 2023

Language models can be trained to recognize the moral sentiment of text, creating new opportunities to study the role of morality in human life. As interest in language and morality has grown, several ground truth datasets with moral annotations have been released. However, these datasets vary in the method of data collection, domain, topics, instructions for annotators, etc. Simply aggregating such heterogeneous datasets during training can yield models that fail to generalize well. We describe a data fusion framework for training on multiple heterogeneous datasets that improve performance and generalizability. The model uses domain adversarial training to align the datasets in feature space and a weighted loss function to deal with label shift. We show that the proposed framework achieves state-of-the-art performance in different datasets compared to prior works in morality inference.
The Morbid Realities of Social Media: An Investigation into the Narratives Shared by the Deceased Victims of COVID-19
Authors: Habib Hussam , Nithyanand Rishab
Published: June 2023

Social media platforms have had considerable impact on the real world especially during the Covid-19 pandemic. Problematic narratives related to Covid-19 might have caused significant impact on the population specifically due to its association with dangerous beliefs such as anti-vaccination and Covid denial. In this work, we study a unique dataset of Facebook posts by users who shared and believed in such narratives before succumbing to Covid-19 often resulting in death. We aim to characterize the dominant themes and sources present in the victim's posts along with identifying the role of the platform in handling deadly narratives. Our analysis reveals the overwhelming politicization of Covid-19 through the prevalence of anti-government themes propagated by right-wing political and media ecosystem. Furthermore, we highlight the efforts of Facebook's implementation of soft moderation actions intended to warn users of misinformation. Results from this study bring insights into the responsibility of political elites in shaping public discourse and the platform's role in dampening the reach of harmful narratives.
Effect of Feedback on Drug Consumption Disclosures on Social Media
Authors: Jangra Hitkul , Shah Rajiv , Kumaraguru Ponnurangam
Published: June 2023

Deaths due to drug overdose in the US have doubled in the last decade. Drug-related content on social media has also exploded in the same time frame. The pseudo-anonymous nature of social media platforms enables users to discourse about taboo and sometimes illegal topics like drug consumption. User-generated content (UGC) about drugs on social media can be used as an online proxy to detect offline drug consumption. UGC also gets exposed to the praise and criticism of the community. Law of effect proposes that positive reinforcement on an experience can incentivize the users to engage in the experience repeatedly. Therefore, we hypothesize that positive community feedback on a user's online drug consumption disclosure will increase the probability of the user doing an online drug consumption disclosure post again. To this end, we collect data from 10 drug-related subreddits. First, we build a deep learning model to classify UGC as indicative of drug consumption offline or not, and analyze the extent of such activities. Further, we use matching-based causal inference techniques to unravel community feedback's effect on users' future drug consumption behavior. We discover that 84% of posts and 55% comments on drug-related subreddits indicate real-life drug consumption. Users who get positive feedback generate up to two times more drugs consumption content in the future. Finally, we conducted an anonymous user study on drug-related subreddits to compare members' opinions with our experimental findings and show that user tends to underestimate the effect community peers can have on their decision to interact with drugs.
Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks
Authors: Jiang Julie , Ren Xiang , Ferrara Emilio
Published: June 2023

Estimating the political leanings of social media users is a challenging and ever more pressing problem given the increase in social media consumption. We introduce Retweet-BERT, a simple and scalable model to estimate the political leanings of Twitter users. Retweet-BERT leverages the retweet network structure and the language used in users' profile descriptions. Our assumptions stem from patterns of networks and linguistics homophily among people who share similar ideologies. Retweet-BERT demonstrates competitive performance against other state-of-the-art baselines, achieving 96%-97% macro-F1 on two recent Twitter datasets (a COVID-19 dataset and a 2020 United States presidential elections dataset). We also perform manual validation to validate the performance of Retweet-BERT on users not in the training data. Finally, in a case study of COVID-19, we illustrate the presence of political echo chambers on Twitter and show that it exists primarily among right-leaning users. Our code is open-sourced and our data is publicly available.
InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks
Authors: Kim Seungbae , Jiang Jyun-Yu , Han Jinyoung , Wang Wei
Published: June 2023

As influencers play considerable roles in social media marketing, companies increase the budget for influencer marketing. Hiring effective influencers is crucial in social influencer marketing, but it is challenging to find the right influencers among hundreds of millions of social media users. In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. To represent the posting behaviors and social relations, the graph convolutional neural networks are applied to model influencers with heterogeneous networks during different historical periods. By learning the network structure with the embedded node features, InfluencerRank can derive informative representations for influencers at each period. An attentive recurrent neural network finally distinguishes highly effective influencers from other influencers by capturing the knowledge of the dynamics of influencer representations over time. Extensive experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months. The experimental results demonstrate that InfluencerRank outperforms existing baseline methods. An in-depth analysis further reveals that all of our proposed features and model components are beneficial to discover effective influencers.
Large-Scale Demographic Inference of Social Media Users in a Low-Resource Scenario
Authors: Lasri Karim , Tonneau Manuel , Naushan Haaya , Malhotra Niyati , Farouq Ibrahim , Orozco-Olvera Víctor , Fraiberger Samuel
Published: June 2023

Characterizing the demographics of social media users
enables a diversity of applications, from better targeting of policy interventions to the derivation of representative population
estimates of social phenomena. Achieving high performance with supervised learning, however, can be challenging as labeled data is often scarce. Alternatively, rule-based matching strategies provide well-grounded information but only offer partial coverage over users. It is unclear, therefore, what features and models are best suited to maximize coverage over a large set of users while maintaining high performance. In this paper, we develop a cost-effective strategy for large-scale demographic inference by relying on minimal labeling efforts. We combine a name-matching strategy with graph-based methods to map the demographics of 1.8 million Nigerian Twitter users.
Specifically, we compare a purely graph-based propagation model, namely Label Propagation (LP), with Graph Convolutional Networks (GCN), a graph model that also incorporates node features based on user content.
We find that both models largely outperform supervised learning approaches based purely on user content that lack graph information. Notably, we find that LP achieves comparable performance to the state-of-the-art GCN while providing greater interpretability at a lower computing cost. Moreover, performance does not significantly improve with the addition of user-specific features, such as textual representations of user tweets and user geolocation. Leveraging our data collection effort, we describe the demographic composition of Nigerian Twitter finding that it is a highly non-uniform sample of the general Nigerian population.
Associative Inference Can Increase People’s Susceptibility to Misinformation
Authors: Lee Sian , Seo Haeseung , Lee Dongwon , Xiong Aiping
Published: June 2023

Associative inference is an adaptive, constructive process of memory that allows people to link related information to make novel connections. We conducted three online human-subjects experiments investigating participants’ susceptibility to associatively inferred misinformation and its interaction with their cognitive ability and how news articles were presented. In each experiment, participants completed recognition and perceived accuracy rating tasks for the snippets of news articles in a tweet format across two phases. At Phase 1, participants viewed real news only. At Phase 2, participants viewed both real and fake news. Critically, we varied whether the fake news at Phase 2 was inferred from (i.e., associative inference), associated with (i.e., association only), or irrelevant to (i.e., control) the corresponding real news pairs at Phase 1. Both recognition and perceived accuracy results showed that participants in the associative inference condition were more susceptible to fake news than those in the other conditions. Furthermore, hashtags embedded within the tweets made the obtained effects evident only for the participants of higher cognitive ability. Our findings reveal that associative inference can be a basis for individuals’ susceptibility to misinformation, especially for those of higher cognitive ability. We conclude by discussing the implications of our results for understanding and mitigating misinformation on social media platforms.
"Learn the Facts about COVID-19": Analyzing the Use of Warning Labels on TikTok Videos
Authors: Ling Chen , Gummadi Krishna P. , Zannettou Savvas
Published: June 2023

During the COVID-19 pandemic, health-related misinformation and harmful content shared online had a significant adverse effect on society.
In an attempt to mitigate this adverse effect, mainstream social media platforms like Facebook, Twitter, and TikTok employed soft moderation interventions (i.e., warning labels) on potentially harmful posts.
Such interventions aim to inform users about the post's content without removing it, hence easing the public's concerns about censorship and freedom of speech.
Despite the recent popularity of these moderation interventions, as a research community, we lack empirical analyses aiming to uncover how these warning labels are used in the wild, particularly during challenging times like the COVID-19 pandemic.

In this work, we analyze the use of warning labels on TikTok, focusing on COVID-19 videos.
First, we construct a set of 26 COVID-19 related hashtags, and then we collect 41K videos that include those hashtags in their description.
Second, we perform a quantitative analysis on the entire dataset to understand the use of warning labels on TikTok.
Then, we perform an in-depth qualitative study, using thematic analysis, on 222 COVID-19 related videos to assess the content and the connection between the content and the warning labels.
Our analysis shows that TikTok broadly applies warning labels on TikTok videos, likely based on hashtags included in the description (e.g., 99% of the videos that contain #coronavirus have warning labels).
More worrying is the addition of COVID-19 warning labels on videos where their actual content is not related to COVID-19 (23% of the cases in a sample of 143 English videos that are not related to COVID-19).
Finally, our qualitative analysis on a sample of 222 videos shows that 7.7% of the videos share misinformation/harmful content and do not include warning labels, 37.3% share benign information and include warning labels, and that 35% of the videos that share misinformation/harmful content (and need a warning label) are made for fun.
Our study demonstrates the need to develop more accurate and precise soft moderation systems, especially on a platform like TikTok that is extremely popular among people of younger age.
"Dummy Grandpa, Do You Know Anything?": Identifying and Characterizing Ad Hominem Fallacy Usage in the Wild
Authors: Patel Utkarsh , Mukherjee Animesh , Mondal Mainack
Published: June 2023

Today, participating in discussions on online forums is extremely commonplace and these discussions have started rendering a strong influence on the overall opinion of online users. Naturally, twisting the flow of the argument can have a strong impact on the minds of naive users, which in the long run might have socio-political ramifications, for example, winning an election or spreading targeted misinformation. Thus, these platforms are potentially highly vulnerable to malicious players who might act individually or as a cohort to breed fallacious arguments with a motive to sway public opinion. Ad hominem arguments are one of the most effective forms of such fallacies. Although a simple fallacy, it is effective enough to sway public debates in offline world and can be used as a precursor to shutting down the voice of opposition by slander.
In this work, we take a first step in shedding light on the usage of ad hominem fallacies in the wild. First, we build a powerful ad hominem detector based on transformer architecture with high accuracy (F1 more than 83%, showing a significant improvement over prior work), even for datasets for which annotated instances constitute a very small fraction. We then used our detector on 265k arguments collected from the online debate forum – CreateDebate. Our crowdsourced surveys validate our in-the-wild predictions on CreateDebate data (94% match with manual annotation). Our analysis revealed that a surprising 31.23% of CreateDebate content contains ad hominem fallacy, and a cohort of highly active users post significantly more ad hominem to suppress opposing views. Then, our temporal analysis revealed that ad hominem argument usage increased significantly since the 2016 US Presidential election, not only for topics like Politics, but also for Science and Law. We conclude by discussing important implications of our work to detect and defend against ad hominem fallacies.
Spillover of Antisocial Behavior from Fringe Platforms: The Unintended Consequences of Community Banning
Authors: Russo Giuseppe , Verginer Luca , Horta Ribeiro Manoel , Casiraghi Giona
Published: June 2023

Online platforms face pressure to keep their communities civil and respectful.
Thus, banning problematic online communities from mainstream platforms is often met with enthusiastic public reactions.
However, this policy can lead users to migrate to alternative fringe platforms with lower moderation standards and may reinforce antisocial behaviors.
As users of these communities often remain co-active across mainstream and fringe platforms, antisocial behaviors may spill over onto the mainstream platform.
We study this possible spillover by analyzing 70,000 users from three banned communities that migrated to fringe platforms: r/The_Donald, r/GenderCritical, and r/Incels.
Using a difference-in-differences design, we contrast co-active users with matched counterparts to estimate the causal effect of fringe platform participation on users' antisocial behavior on Reddit.
Our results show that participating in the fringe communities increases users' toxicity on Reddit (as measured by Perspective API) and involvement with subreddits similar to the banned community---which often also breach platform norms.
The effect intensifies with time and exposure to the fringe platform.
In short, we find evidence for a spillover of antisocial behavior from fringe platforms onto Reddit via co-participation.
Social Influence-Maximizing Group Recommendation
Authors: Sun Yangke , Cautis Bogdan , Maniu Silviu
Published: June 2023

In this paper, we revisit the group recommendation problem, by taking into consideration the information diffusion in a social network, as one of the main criteria that must be maximised. While the well-known influence maximization problem has the objective to select k users (spread seeds) from a social network, so that a piece of information can spread to the largest possible number of people in the network, in our setting the seeds are known (given as a group), and we must decide which k items (pieces of information) should be recommended to them. Therefore, the recommended items should at the same time be the best match for that group's preferences, and have the potential to spread as much as possible in an underlying diffusion network, to which the group members (the seeds) belong. This problem is directly motivated by group recommendation scenarios where social networking is an inherent dimension that must be taken into account when assessing the potential impact of a certain recommendation. We present the model and formulate the problem of influence-aware group recommendation as a multiple objective optimization problem. We then describe a greedy approach for this problem and we design an optimisation approach, by adapting the top-k algorithms NRA and TA. We evaluate all these methods experimentally, in three different recommendation scenarios, for movie, micro-blog and book recommendations, based on real-world datasets from Flixster, Twitter, and Douban respectively. Unsurprisingly, with the introduction of information diffusion as an optimization criterion for group recommendation, the recommendation problem becomes more complex. However, we show that our algorithms enable spread efficiency without loss of recommendation precision, under reasonable latency.
A Multi-Task Model for Sentiment Aided Stance Detection of Climate Change Tweets
Authors: Upadhyaya Apoorva , Fisichella Marco , Nejdl Wolfgang
Published: June 2023

Climate change has become one of the biggest challenges of our time. Social media platforms such as Twitter play an important role in raising public awareness and spreading knowledge about the dangers of the current climate crisis. With the increasing number of campaigns and communication about climate change through social media, the information could create more awareness and reach the general public and policy makers. However, these Twitter communications lead to polarization of beliefs, opinion-dominated ideologies, and often a split into two communities of climate change deniers and believers. In this paper, we propose a framework that helps identify denier statements on Twitter and thus classifies the stance of the tweet into one of the two attitudes towards climate change (denier/believer). The sentimental aspects of Twitter data on climate change are deeply rooted in general public attitudes toward climate change. Therefore, our work focuses on learning two closely related tasks: Stance Detection and Sentiment Analysis of climate change tweets. We propose a multi-task framework that performs stance detection (primary task) and sentiment analysis (auxiliary task) simultaneously. The proposed model incorporates the feature-specific and shared-specific attention frameworks to fuse multiple features and learn the generalized features for both tasks. The experimental results show that the proposed framework increases the performance of the primary task, i.e., stance detection by benefiting from the auxiliary task, i.e., sentiment analysis compared to its uni-modal and single-task variants.
An Open-Source Cultural Consensus Approach to Name-Based Gender Classification
Authors: Van Buskirk Ian , Clauset Aaron , Larremore Daniel B.
Published: June 2023

Name-based gender classification has enabled hundreds of otherwise infeasible scientific studies of gender. Yet, the lack of standardization, reliance on paid services, understudied limitations, and conceptual debates cast a shadow over many applications. To address these problems we develop and evaluate an ensemble-based open-source method built on publicly available data of empirical name-gender associations. Our method integrates 36 distinct sources—spanning over 150 countries and more than a century—via a meta-learning algorithm inspired by Cultural Consensus Theory (CCT). We also construct a taxonomy with which names themselves can be classified. We find that our method's performance is competitive with paid services and that our method, and others, approach the upper limits of performance; we show that conditioning estimates on additional metadata (e.g. cultural context), further combining methods, or collecting additional name-gender association data is unlikely to meaningfully improve performance. This work definitively shows that name-based gender classification can be a reliable part of scientific research and provides a pair of tools, a classification method and a taxonomy of names, that realize this potential.
Conversation Modeling to Predict Derailment
Authors: Yuan Jiaqing , Singh Munindar P.
Published: June 2023

Conversations among online users sometimes derail, i.e., break down into personal attacks. Derailment interferes with the healthy growth of communities in cyberspace. The ability to predict whether an ongoing conversation will derail could provide valuable advance, even real-time, insight to both interlocutors and moderators. Prior approaches predict conversation derailment retrospectively without the ability to forestall the derailment proactively. Some existing works attempt to make dynamic predictions as the conversation develops, but fail to incorporate multisource information, such as conversational structure and distance to derailment.

We propose a hierarchical transformer-based framework that combines utterance-level and conversation-level information to capture fine-grained contextual semantics. We propose a domain-adaptive pretraining objective to unite conversational structure information and a multitask learning scheme to leverage the distance from each utterance to derailment. An evaluation of our framework on two conversation derailment datasets shows an improvement in F1 score for the prediction of derailment. These results demonstrate the effectiveness of incorporating multisource information for predicting the derailment of a conversation.
A Multi-Platform Collection of Social Media Posts about the 2022 U.S. Midterm Elections
Authors: Aiyappa Rachith , DeVerna Matthew R. , Pote Manita , Tran Truong Bao , Zhao Wanying , Axelrod David , Pessianzadeh Aria , Kachwala Zoher , Kim Munjung , Can Seckin Ozgur , Kim Minsuk , Gandhi Sunny , Manikonda Amrutha , Pierri Francesco , Menczer Filippo , Yang Kai-Cheng
Published: June 2023

Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.
Truth Social Dataset
Authors: Gerard Patrick , Botzer Nicholas , Weninger Tim
Published: June 2023

Formally announced to the public following former President Donald Trump’s bans and suspensions from mainstream social networks in early 2022 following his role in the January 6 Capitol Riots, Truth Social was launched as an ``alternative'' social media platform that claims to be a refuge for free speech, offering a platform for those disaffected by the content moderation policies of then existing, mainstream social networks. The subsequent rise of Truth Social has been driven largely by hard-line supporters of the former president as well as those affected by the content moderation of other social networks. These distinct qualities combined with the its status as the main mouthpiece of the former president positions Truth Social as a particularly influential social media platform and give rise to several research questions. However, outside of a handful of news reports, little is known about the new social media platform partially due to a lack of well-curated data. In the current work, we describe a dataset of over 823,000 posts to Truth Social and and social network with over 454,000 distinct users. In addition to the dataset itself, we also present some basic analysis of its content, certain temporal features, and its network.
Auditing Elon Musk’s Impact on Hate Speech and Bots
Authors: Hickey Daniel , Schmitz Matheus , Fessler Daniel , Smaldino Paul E. , Muric Goran , Burghardt Keith
Published: June 2023

On October 27th, 2022, Elon Musk purchased Twitter, becoming its new CEO and firing many top executives in the process. Musk listed fewer restrictions on content moderation and removal of spam bots among his goals for the platform. Given findings of prior research on moderation and hate speech in online communities, the promise of less strict content moderation poses the concern that hate will rise on Twitter. We examine the levels of hate speech and prevalence of bots before and after Musk's acquisition of the platform. We find that hate speech rose dramatically upon Musk purchasing Twitter and the prevalence of most types of bots increased, while the prevalence of astroturf bots decreased.
The Amplification Paradox in Recommender Systems
Authors: Horta Ribeiro Manoel , Veselovsky Veniamin , West Robert
Published: June 2023

Automated audits of recommender systems found that blindly following recommendations leads users to increasingly partisan, conspiratorial, or false content. At the same time, studies using real user traces suggest that recommender systems are not the primary driver of attention toward extreme content; on the contrary, such content is mostly reached through other means, e.g., other websites. In this paper, we explain the following apparent paradox: if the recommendation algorithm favors extreme content, why is it not driving its consumption? With a simple agent-based model where users attribute different utilities to items in the recommender system, we show through simulations that the collaborative-filtering nature of recommender systems and the nicheness of extreme content can resolve the apparent paradox: although blindly following recommendations would indeed lead users to niche content, users rarely consume niche content when given the option because it is of low utility to them, which can lead the recommender system to deamplify such content. Our results call for a nuanced interpretation of "algorithmic amplification" and highlight the importance of modeling the utility of content to users when auditing recommender systems. Code available: https://github.com/epfl-dlab/amplification_paradox.
Identification and Validation of Key Gene Modules and Pathways in Coronary Artery Disease Development and Progression
Authors: Yoon Ewnji , Zhang Wenjing , Cai Yunpeng , Peng Changnong , Zhou Daxin
Published: June 2023

The development and progression of atherosclerosis represent a chronic process involving complex molecular interactions. Therefore, identifying the potential hub genes and pathways contributing to coronary artery disease (CAD) development is essential for understanding its underlying molecular mechanisms. To this end, we performed transcriptome analysis of peripheral venous blood collected from 100 patients who were divided into four groups according to disease severity, including 27 patients in the atherosclerosis group, 22 patients in the stable angina group, 35 patients in the acute myocardial infarction group, and 16 controls. Weighted gene co-expression network analysis was performed using R programming. Significant module-trait correlations were identified according to module membership and genetic significance. Metascape was used for the functional enrichment of differentially expressed genes between groups, and the hub genes were identified via protein-protein interaction network analysis. The hub genes were further validated by analyzing Gene Expression Omnibus (GSE48060 and GSE141512) datasets. A total of 9,633 messenger ribonucleic acids were detected in three modules, among which the blue module was highly correlated with the Gensini score. The hub genes were significantly enriched in the myeloid leukocyte activation pathway, suggesting its important role in the progression of atherosclerosis. Among these genes, the Mediterranean fever gene (MEFV) may play a key role in the progression of atherosclerosis and CAD severity.
STUDY OF THE INFLUENCE OF THE SHAPE OF THE COPPER COMPONENT ON THE PROPERTIES OF THE FRICTION MATERIAL USED IN BRAKES. PART III: HEATING PROCESS DURING BRAKING
Authors: Borawski Andrzej
Published: June 2023

The ability to effectively stop the vehicle on a short distance is one of the most important features of today's automotive industry. To make this possible, various and extensive researches are carried out to ensure the correct selection of the friction material. The state of today's natural environment and the related to it new restrictive regulations require friction materials to be as ecologically friendly as possible during operation, as well as during production or recycling. This leads to the search for new solutions. One of such solutions is proposed in this paper. It consists of replacing one of the main components, the reinforcement, with one that is present in the pads anyway - copper. Instead of using it in the form of a powder, it was decided to use it in the form of fibers. Three different materials were proposed, in which, among others, fiber length was regulated. The samples produced allowed one to determine the necessary material data. On their basis, simulation tests were carried out which showed that copper in the form of fibers can successfully replace the previously used materials such as aramid or carbon fiber.
The role of hydraulic and thermal properties of soil in evaporation: a numerical insight
Authors: Guida Giulia , Vespo Vincenzo Sergio , Musso Guido , Della Vecchia Gabriele
Published: June 2023

Evaporation from geological formations results from the interaction between the geomaterial and the atmosphere. Geotechnical engineering issues, such as slope stability, pollution containment and soil heave/shrinkage, require a deep understanding of the soil–atmosphere interaction ruled by evaporation. Evaporation is a multiphase thermo-hydraulic phenomenon that includes liquid water, vapour and heat fluxes. It is generally modelled considering the thermal energy and water mass balance equations of unsaturated soils. This paper presents a numerical model for reproducing evaporation processes under controlled environmental conditions. The model was implemented in the Comsol Multiphysics finite-element software and first validated against experimental data from the literature. Then, it was used to investigate the role of hydraulic and thermal properties in the evaporative response. The numerical results revealed differences in the evolution of the water content profiles over time due to the interplay between hydraulic conductivity and retention properties. Hydraulic conductivity mainly impacts the shape of water content isochrones: fast drying of superficial layers and slow desaturation of deeper layers occur with decreasing hydraulic conductivity values. On the other hand, the moisture capacity primarily impacts the thickness of the desaturating layer, which decreases for higher values of the moisture capacity.
Botulunim Toxin Type A Followed by TAR (Transversus Abdominal Release) with Abdominoplasty as the Best Combination for Big Hernia Repair
Authors: A Araujo-Lopez , PCP Salazar-Del , RR Peña-González , P Cardona-Ochoa , M Gonzalez-Rodriguez , RL Rios-Lara , R Torres-Piña , JA Ugalde-Vitelly
Published: June 2023

Introduction: The current incidence of incisional hernia is up to 11%. Botulinum toxin type A shows atrophy in the extrafusal and intrafusal muscle fibers. With the addition of Muscle Paralysis and improving strength, it is possible to perform an effective posterior component separation. Objective: Describe an easy algorithm to treat complex abdominal wall reconstruction and improve the quality of adjacent tissues. Material and Methods: Fifty-nine patients were analyzed over 1 year in the General Surgery and Plastic and Reconstructive Surgery Service, patients with midline abdominal wall hernias, using the effective reconstruction algorithm, placing by ultrasound-guided botulinum toxin in the muscle transverse, 1 month later performing TAR, with Abdominoplasty for skin flap management, and comparing with the control group that did not apply botulinum toxin. Discussion: Botox is a neurotoxin derived from the bacterium Clostridium botulinum (botulinum toxin type A) that has been observed at the sensory level causing atrophy in the extrafusal and intrafusal muscle fibers. Repair of the abdominal wall after incisional hernias has been a great challenge with recurrence rates of 11%. Performing the release of the transverse muscle has a recurrence of 6%, as well as the compensation of skin flaps are ideal for proper management. Results: Within the observed analyzes, the results were divided into trans-surgical (99% of the patients closed the midline), immediate post-surgical (Pain, Seroma and Infection <1%) and late (with <3% recurrence) in the experimental group. Conclusions: Within the study, we will be able to analyze that lowering the recurrence rate <3% in patients properly protocolized, is translated as an adequate and totally reproducible method in our environment.
RECENT ADVANCES IN PLANNING FARM OPERATIONS THROUGH OPTIMIZATION MODELS
Authors: YILDIRIM Yunus , ULUCAN Aydın , ATICI Kazım Barış
Published: June 2023

Operations Research applications in the agriculture sector have been a research area of high interest for over 50 years. Due to food security and sustainability concerns in the world, a lot of attention has been given to this area by OR researchers and practitioners recently. From distribution planning to performance evaluation, a variety of approaches and methods have been applied to a broad range of agricultural problems. Therefore, many review papers have been published from different points of view to serve both general and specific academic purposes. In this work, we present a review of the optimization approaches for the planning of farming operations which aims to optimize agricultural production systems. We use Scopus database to find relevant studies in three decision areas: crop planning, harvest planning and machinery management. Our review covers 54 papers published between 2002-2022.