bluuebunny/crossref_metadata_2025 huggingface dataset:
THE PROSPECTS OF NORTHERN IRELAND POLITICAL STABILISATION
Authors:   Institute of World history RAS, Polyakova Elena
Published: May 2024

The article deals with the reasons of two years political crisis in Northern Ireland. According to the author, it became possible due to Belfast Agreement power-sharing system which means joint government on a cross-community basis between representatives of protestant and catholic traditions. If one of the side ceased to hold office by any reason, the other shall cease to function immediately. The main reason for political crises in February 2022 was the demand of the Democratic unionist party to reconsider the Northern Ireland protocol – part of the Brexit Agreement, and their refusal to hold office. The absence of the Executive led to economic destabilisation, have a negative impact on social situation. Lengthy negotiations between DUP, British, Irish governments and EU resulted in step-by-step changes in protocol and resumption of work in Stormont in February 2024. To avoid further crises it’s required to reform the political institutions.
Different development of global warming (GW) and urban heat island (UHI) in the city of Zagreb
Authors: Bonacci Ognjen , Vrsalović Adrijana , Roje-Bonacci Tanja
Published: May 2024

The analysis examines various impacts of global warming (GW) and urban heat island (UHI) effects at four meteorological stations in and around Zagreb, Croatia, spanning from 1981 to 2021. The goal was to determine the intensity and causes of the different warming tendencies and behaviour of air temperature variations observed in various parts of Zagreb. Data from four meteorological stations were analysed: Grič (GR) – a hill within the city area, Maksimir (MA) – an urbanized suburban area, Pleso (PL) – a suburban area unaffected by intense urbanization (airport), and Puntijarka (PU) – atop of Mt. Medvednica. Various statistical methods were applied to analyse trends and variations in annual, monthly, and daily air temperature indices (ATI), including linear regression, the Mann–Kendall test, the day-to-day variability method (DTD), and diurnal air temperature range (DTR) method. All stations showed increasing trends in all ATI. At the PU station, the trend is solely due to GW; at others, both UHI and GW contribute. The MA station exhibits the most pronounced UHI. DTD variability decreases at all stations. GR sees an increasing DTR trend, while PU's trend is decreasing. No DTR trends were observed at the other two stations. Overall, ATI variations are similar, but each location's behaviour differs due to its unique local environment.
Optimal body mass index for protecting middle-aged and elderly patients with fatty liver from future fractures
Authors: Yeh Hsiao-Yun , Wu Hung-Ta Hondar , Shen Hsiao-Chin , Li Tzu-Hao , Yang Ying-Ying , Lee Kuei-Chuan , Lin Yi-Hsuan , Huang Chia-Chang , Hou Ming-Chih
Published: May 2024

Objective

Previous studies have suggested that body mass index (BMI) should be considered when assessing the relationship between fatty liver (FL) and osteoporosis. The aim of this study was to investigate future fracture events in people with FL, focusing on the effect of BMI in both sexes.

Methods

This retrospective cohort study, spanning from 2011 to 2019, enrolled 941 people, including 441 women and 500 men, aged 50 years or older who underwent liver imaging (ultrasound, computed tomography, or magnetic resonance image) and dual-energy X-ray absorptiometry (for bone mineral density measurements). The study examined predictors of osteoporosis in both sexes and the effect of different ranges of BMI (18.5–24, 24–27, and ≥27 kg/m2) on the risk of future fracture events in FL patients.

Results

The average follow-up period was 5.3 years for women and 4.2 years for men. Multivariate analysis identified age and BMI as independent risk factors of osteoporosis in both sexes. Each unit increase in BMI decreased the risk of osteoporosis by ≥10%. In both women and men with FL, a BMI of 24–27 kg/m2 offered protection against future fractures, compared to those without FL and with a BMI of 18.5–24 kg/m2.

Conclusion

The protective effect of a higher BMI against future fractures in middle-aged and elderly female and male patients with FL is not uniform and diminishes beyond certain BMI ranges.
Evaluation of indoor radon gas levels in three Brazilian municipalities located in the state of Minas Gerais
Authors: Otero Ubirani , Da Silva Nivaldo Carlos , Neves da Cunha Tarcisio , Geraldino Barbara , Navarro Antoniazzi Berenice
Published: May 2024

Ionizing radiation, particularly radon exposure, constitutes a significant risk factor for lung cancer, with the World Health Organization identifying radon as the second major contributor to this malignancy. This study investigates radon levels in dwellings across three Brazilian municipalities and explores the interplay between dwelling characteristics, residents' behavior, and radon concentrations equal to or exceeding 200 Bq/m³. Conducted over two seasons, the research measured radon gas concentrations in both the bedroom and living room of each residence. Out of 577 households surveyed, 123 exhibited radon levels meeting or surpassing 200 Bq/m³, with a higher incidence observed in rural areas. Factors associated with elevated radon levels included geographical location in Caldas and Poços de Caldas, residence in rural areas, houses constructed before 1976, and the absence of open windows during the night. The study underscores the prevalence of elevated indoor radon levels, surpassing the World Health Organization's recommended reference level of 100 Bq/m³. Additionally, dwelling characteristics and residents' habits, such as keeping windows closed during sleep, contribute to increased radon concentrations in residential settings.
Optimal Scale Points for Reliable Measurements: Exploring the Impact of Scale Point Variation
Authors: Ismail Raoda , Retnawati Heri , Sugiman Sugiman , Setiawati Farida Agus , Imawan Okky Riswandha , Santoso Purwoko Haryadi
Published: May 2024

Ensuring reliable measurements is crucial for minimising errors in assessments. The assessmentcommunity commonly employs the evaluation of reliability coefficients to estimate the dependability oftest scores. Despite its significance, limited research has explored the relationship between the estimated reliability coefficient and the number of scale points utilised. This study aims to provide valuable insights to practitioners by investigating the optimal number of scale points required for the most accurate reliability coefficient estimation. Using simulated data, the research scrutinises scales with varying points, ranging from 2 to 11. The results reveal a substantial impact of the number of scale points on reliability estimation. The most accurate estimate of reliability is obtained for scales with 8 points. This study helps us understand the optimal number of scale points for reliable measurements and guides future assessment improvements.
Prevalence and molecular characterisation of Shiga toxin-producing Escherichia coli in sheep farms of Sanandaj, Iran
Authors: Ghaderi P. , Ahmadi E. , Farrokhi A. M. , Moshrefi F. , Rezaei A. , Siavashi K. , Ghavami Q. , Rahmani K. , Sharifi A.
Published: May 2024

Shiga toxin-producing Escherichia coli (STEC) strains have emerged as important foodborne pathogens of global public health concern, causing life-threatening diseases. Animals and their products have been documented as important reservoirs for STECs, especially E. coli O157. The aim of this study was to investigate STECs from healthy and diarrhoeic sheep in Sanandaj, Iran. In the current study, a total of 81 sheep faecal samples were taken (22 from diarrhoeic sheep and 59 from healthy sheep). E. coli and subsequently STEC strains was detected according to standard protocol (cultural characterisation and PCR assays). Finally, the frequency of Shiga-toxin producing gene(s) (stx1, stx2), intimin (eaeA) and enterohaemolysin (hlyA) was detected among STEC isolates using duplex PCR. Totally, 42 E. coli were isolated from 81 faecal samples (51.85% contamination). Of these, 34 isolates (80.9%) were identified as STEC patotypes based on Sorbitol-MacConkey (SMAC) medium culturing and also the presence of stx1 and/or stx2. Of these, only 3 isolates (7.1%) were identified as serotype O157:H7 based on PCR assay. In addition, the results showed that STEC bacteria were significantly more prevalent in diarrhoeic samples than in healthy samples (50% vs. 22.1%). Overall, the PCR results showed that 33 (97%), 12 (35.3%) and 8 (23.5%) isolates carried stx1, stx2 and hlyA, respectively. The eaeA gene was not found in any isolate. The number of isolated STEC bacteria in spring (10 isolates) and winter (14 isolates) were significantly higher than those in summer (4 isolates) and autumn (6 isolates) (P=0.039). Also, the number of STEC in diarrhoea samples was significantly higher compared to non-diarrhoea samples (P=0.032). In conclusion, the present study revealed high prevalence rate of STEC including serotype O157:H7 and non-O157:H7 in sheep faeces which highlights the importance of sheep as a reservoir of STEC pathogen in Sanandaj region. Therefore, additional control and preventive measures must be undertaken to control the contamination by this pathogen.
An Evangelical Group’s Experience of Australian Secular Workplaces
Authors: Hussey Ian
Published: May 2024

This study employed the Qualitative Description methodology to investigate how a sub-group of Australian evangelicals associated with the City Bible Forum navigate their secular workplaces utilizing an anonymous online survey, discussion groups, and interviews. Compared to broader Christian samples, participants demonstrated greater faith–work integration. They were more likely to perceive colleagues as bearing God’s image and to display greater work diligence than other Australian Christians. However, they placed less emphasis on workplace creativity and deriving personal worth from their work. Their theological outlook centred on serving the Lord, ethical conduct, caring for colleagues, and witnessing. Yet, references to the creational mandate and sensing God’s presence in work were infrequent. These evangelicals found their workplaces both strengthening and challenging due to LGBTQIA+ initiatives in their organizations. Although faith-based persecution or micro-aggressions were rare, the study affirmed Paul Bramadat’s (2000) bridging and fortressing strategies. Christian Smith’s (2002) and Michael Lindsay and Bradley Smith’s (2010) work further interpreted the qualitative description.
Omar Kasmani, Queer Companions: Religion, Public Intimacy, and Saintly Affects in Pakistan
Authors: Variyar Suvarna
Published: May 2024

Omar Kasmani, Queer Companions: Religion, Public Intimacy, and Saintly Affects in Pakistan. Durham, NC: Duke University Press, 2022, pp. xvi + 208, US$99.95 (hb), US$25.95 (pb), ISBN 978-1-4780-1541-3 (hb), ISBN 978-1-4780-1803-2 (pb), ISBN 978-1-4780-2265-7 (eb).
Introduction to the Special Issue
Authors: Milani Milad
Published: May 2024

Introduction to the Special Issue: New Perspectives on Religions and Traditions
Peran Mahkamah Konstitusi Sebagai Pelaku Sistem Kekuasaan Kehakiman di Indonesia
Authors: Suci Wulandari  , Pingkan Utari  , Fergio Rizkya Refin  , Moh. Bagus  , Akhmad Fandik  , Thobary Amim
Published: May 2024

The Constitutional Court is one of the highest state institutions in Indonesia as an actor in the judiciary along with the Supreme Court. The Constitutional Court has the authority to examine, examine, and decide on laws deemed to be contrary to the 1945 Constitution of the Republic of Indonesia. Therefore, decisions from the Constitutional Court are final and binding in that there are no other legal remedies. In order to review the law against the 1945 Constitution of the Republic of Indonesia, both formal and material tests can be carried out. Based on this, this research will provide a new reflection on how the Constitutional Court enforces the law and provides constitutional protection to every citizen in accordance with the legal basis in Indonesia. This study uses a normative juridical approach and materials taken through literature studies. The aim of this research is to find out about the Constitutional Court and the Judicial Power System in Indonesia. Through descriptive analysis, the author then collects reading sources through relevant laws, books and journals. Then, the literature material is processed using an inductive method where the researcher examines and draws conclusions from a case. From the results of this research, it turns out that the Constitutional Court is not only an actor of judicial power but also a protector of citizens' constitutional rights so that they are not violated.
Wingsail design methodology and performance evaluation metrics for autonomous sailing
Authors:   ,   Massachusetts Institute of Technology, Cole Blake I. B. ,   Woods Hole Oceanographic Institution
Published: May 2024

This dissertation explores an innovative approach to the design of aerodynamically-actuated wingsails, along with advancements in marine vehicle autonomy focused on vessel tracking and collision avoidance. The first segment of this research introduces a deterministic wingsail design optimization framework that leverages geometric programming to efficiently generate optimal wingsail designs. The proposed methodology is then validated through the development and deployment of a bespoke wingsail data acquisition system called the WingDAQ, which enables quantitative analysis of the unsteady forces affecting wingsail performance in realistic sailing conditions. The results highlight the limitations of traditional aerodynamic models due to unsteady flow conditions, and suggest that lighter wingsails designed with higher lift-to-drag ratios and faster natural frequencies enhance sailing performance. This thesis also introduces a novel instantiation of the unscented Kalman filter (UKF) that is particularly well suited to long-distance surface vessel tracking, and facilitates affordable collision avoidance capabilities on autonomous surface vessels. One such low-cost collision avoidance system is presented, utilizing real-time data from the Automatic Information System (AIS) and the behavior-based autonomy middleware, MOOS-IvP. Field tests confirm the robustness of this framework, establishing a foundation for the integration of physics-based design optimization and adaptive autonomy for windpowered marine vehicles.
Obesity outcomes on electrocardiographic, echocardiographic, and blood pressure parameters in cats
Authors: Martins Patricia L. , Araújo Steffi L. , Pereira Thyago H.S. , Silva Isaac N.G. , Morais Glayciane B. , Evangelista Janaina S.A.M.
Published: May 2024

ABSTRACT: Obesity is considered a chronic inflammatory process that is related to metabolic impairment, respiratory distress, and cardiovascular disease. In cats, few studies have evaluated the implications of obesity on the cardiovascular system, and the existing literature is controversial. Therefore, the aim of this study is to detect pressure, electrocardiographic, and morphofunctional changes in overweight and obese cats. After clinical and laboratory evaluation, 45 animals were selected for the study. Cats were separated according to body condition score (BCS) into three groups (control, overweight, and obese). All animals underwent blood pressure measurement, electrocardiogram, and transthoracic echocardiogram. Results are presented as mean ± standard deviation (SD). Data were considered statistically significant at p<0.05. GraphPad Prism® 7.04 software was used for statistical analyses. Mean arterial pressure in obese cats was higher than in animals with ideal weight (CT 123.60 ± 8.97mmHg vs OB 143.00 ± 22.12mmHg, p<0.0138), but hypertension was not detected. On the electrocardiogram, P wave duration (CT 33.67 ± 1.56ms vs OB 37.76 ± 2.76ms; p<0.0003) and QRS complex (CT 48.14 ± 2.56ms vs OB 54.48 ± 5.51ms; p<0.002) were significantly higher in the obese group. There were no significant echocardiographic changes. There was a direct correlation between blood pressure and BCS (r:0.36, p<0.01). The P wave was positively correlated with the BCS (r:0.56, p<0.0001). Likewise, the QRS complex correlated directly with the BCS (r:0.52, p<0.0003). The results indicate cat obesity can directly affect the cardiovascular system, promoting pressure and electrocardiographic changes. Therefore, monitoring the cardiovascular system of cats with obesity is essential.
Helping hands: intentional communities and social capital development. Evidence from Portugal
Authors: Conceição Tiago , Mota Luís F. , Pinto Maria Luís
Published: May 2024

ABSTRACT Introduction: Building upon the theoretical frameworks of Pierre Bourdieu and Nan Lin regarding social capital, this study examined the capacity of two Portuguese intentional communities (ICs) to cultivate and promote social capital. Our goal was to assess the decommodification potential of these alternative social structures and their role in mitigating social inequality. Materials and methods: Our data collection involved a combination of participant observation and semi-structured interviews. Additionally, we employed slightly adapted versions of social capital mapping and measurement techniques, incorporating the identification of contacts with prestigious positions/occupations (Position-Generator) and access to relevant resources (Resource-Generator). Results: Our findings indicate that social networks developed in the context of intentional communities can play a crucial role in promoting the accumulation of economic benefits and human capital, as well as in fostering the development of social identity and offering emotional support. However, notable disparities emerged between the two case studies concerning their capacity to reduce market dependence and the value of the different forms of mobilized social capital. In one instance, community economies based on collective, profitable activities effectively addressed basic needs through communal labour. Conversely, in the other case, while community economies based on individual enterprises reduced the personal costs associated with meeting basic needs, this led to increased dependence on external, temporary employment. Within this context, individual social capital, such as intra-community relationships, emerges as pivotal for securing economic gains, such as enhanced employability and the acquisition of new skills. Discussion: The internal structure of intentional communities directly impacts the development of social capital and the reduction of market dependence, thereby playing a crucial role in promoting equality and inclusion. While intentional communities offer emotional benefits, they may also present barriers to entry for individuals with limited financial resources. Continued longitudinal studies are essential to assess whether the initial diversity within these communities translates into social equality and to identify the contributing factors to such outcomes.
O CAUSO DOS CÃES DE PASSAGEM NA ROÇA
Authors: Galindo Dolores Cristina Gomes , Bianchini Maria Leticia de Oliveira , Rodrigues Renata Vilela , Zanata Fernanda
Published: May 2024

Resumo: Este artigo reflete sobre a singularidade das relações entre mulheres rurais e os cães que com elas constituem um relacionamento interespecífico. Objetiva contar sobre práticas que foram emergindo ao longo de uma pesquisa quando passamos a encarar seriamente a agência de animais a partir do cotidiano de uma agricultora pesquisadora alinhada à agroecologia familiar. Focalizamos os cães de passagem sem raça definida e que vão e vêm livremente entre as cercas que delimitam as propriedades rurais. Em diálogo com Donna Haraway, Anna Tsing e Vinciane Despret, apresentamos pistas para uma pesquisa multiespécie que opera por meio de causos. A pesquisa multiespécie nos convida a reflexões sobre práticas anticapitalistas que germinam na agroecologia, a nos determos no trabalho das mulheres agricultoras e dos cães na sua potência de autogoverno e de criarmos formas de cooperação multiespecíficas com alteridades significativas no projeto moderno-colonizador.
Decentralised Moderation for Interoperable Social Networks: A Conversation-Based Approach for Pleroma and the Fediverse
Authors: Agarwal Vibhor , Raman Aravindh , Sastry Nishanth , M. Abdelmoniem Ahmed , Tyson Gareth , Castro Ignacio
Published: May 2024

The recent development of decentralised and interoperable social networks (such as the "fediverse") creates new challenges for content moderators. This is because millions of posts generated on one server can easily "spread" to another, even if the recipient server has very different moderation policies. An obvious solution would be to leverage moderation tools to automatically tag (and filter) posts that contravene moderation policies, e.g. related to toxic speech. Recent work has exploited the conversational context of a post to improve this automatic tagging, e.g. using the replies to a post to help classify if it contains toxic speech. This has shown particular potential in environments with large training sets that contain complete conversations. This, however, creates challenges in a decentralised context, as a single conversation may be fragmented across multiple servers. Thus, each server only has a partial view of an entire conversation because conversations are often federated across servers in a non-synchronized fashion. To address this, we propose a decentralised conversation-aware content moderation approach suitable for the fediverse. Our approach employs a graph deep learning model (GraphNLI) trained locally on each server. The model exploits local data to train a model that combines post and conversational information captured through random walks to detect toxicity. We evaluate our approach with data from Pleroma, a major decentralised and interoperable micro-blogging network containing 2 million conversations. Our model effectively detects toxicity on larger instances, exclusively trained using their local post information (0.8837 macro-F1). Yet, we show that this approach does not perform well on smaller instances that do not possess sufficient local training data. Thus, in cases where a server contains insufficient data, we strategically retrieve information (posts or model parameters) from other servers to reconstruct larger conversations and improve results. With this, we show that we can attain a macro-F1 of 0.8826. Our approach has considerable scope to improve moderation in decentralised and interoperable social networks such as Pleroma or Mastodon.
Users’ Behavioral and Emotional Response to Toxicity in Twitter Conversations
Authors: Aleksandric Ana , Saha Roy Sayak , Pankaj Hanani , Wilson Gabriela Mustata , Nilizadeh Shirin
Published: May 2024

Prior works have shown connections between online toxicity attacks, such as harassment, cyberbullying, and hate speech, and the subsequent increase in offline violence, as well as negative psychological effects on victims. These correlations are primarily identified through user studies conducted via virtual environments, simulations, and questionnaires. However, no work has investigated how, in practice and authentically, people react to online toxicity both emotionally, showing anger, anxiety, and sadness, and behaviorally in terms of engaging with and responding to toxicity instigators, considering conversations as a whole and the relation between emotions and behaviors. This data-driven study investigates the effect of toxicity on Twitter users' behaviors and emotions considering confounding factors, such as account identifiability, activity, and conversation's structure and topic. We collected about 80K Twitter conversations and identified those with and without toxic replies. Performing statistical tests along with propensity score matching, we investigated the causal association of receiving toxicity and users' responses. We found that authors of conversations with toxic replies are more likely to engage in conversations, reply in a toxic way, and unfollow toxicity instigators. In terms of users' emotional responses, we found that sadness and anger after the first toxic reply are more likely to increase as the amount of toxicity increases. These findings not only emphasize the negative emotional and behavioral effects of online toxicity on social media users but also, as demonstrated in this paper, can be utilized to build prediction models for users' reactions, which could then aid the implementation of proactive detection and intervention measures helping users in such situations.
MultiFOLD: Multi-source Domain Adaption for Offensive Language Detection
Authors: Arango Aymé , Kaghazgaran Parisa , Sarwar Sheikh Muhammad , Murdock Vanessa , Lee Cj
Published: May 2024

Automatic offensive language detection remains challenging,
and is a crucial part of preserving the openness of digital
spaces, which are an integral part of our everyday experi-
ence. The ever-growing forms of offensive online content
makes traditional supervised approaches harder to scale due
to the financial and psychological costs incurred by collect-
ing human annotations. In this work, we propose a domain
adaptation framework for offensive language detection, Mul-
tiFOLD, which learns and adapts from multiple existing data
sets (or source domains) to an unlabeled target domain. Under
the hood, a curriculum learning algorithm is employed that
kicks off learning with the instances most similar to the target
domain while gradually expanding to more distant instances.
The proposed model is trained with a standard task-specific
loss and a domain adversarial objective which aims to min-
imize the language distinctions across the multiple sources
and the target, allowing the classifier to distinguish offen-
siveness rather than domain. Our experiments on six pub-
licly available data sets demonstrate the effectiveness of Mul-
tiFOLD. Relative improvement in F1 of 0.5% (WOAH) to
29.7% (ICWSM) is found across five out of the six datasets
compared to the state-of-the-art domain adaptation baseline
BERT-DAA, resulting in an average of 6% relative F1-score
gain.
Understanding Community Resilience: Quantifying the Effects of Sudden Popularity via Algorithmic Curation
Authors: Chan Jackie , Lambert Charlotte , Choi Frederick , Chancellor Stevie , Chandrasekharan Eshwar
Published: May 2024

The sudden popularity communities gain via algorithmically-curated "trending'" or "hot" social media feeds can be beneficial or disruptive. On one hand, increased attention often brings new users and promotes community growth. On the other hand, the unexpected influx of newcomers can burden already overworked moderation teams. To examine the impact of sudden popularity, we studied 6,306 posts that reached Reddit's front page---a feed called r/popular that millions of users browse daily---and the effects of sudden popularity within 1,320 subreddits. We find that on average, r/popular posts have 45 times the comments, 42 times the removed comments, and 70 times the number of newcomers compared to posts from the same community that did not reach r/popular. Additionally, r/popular posts led to a peak 85% median increase in the subreddit's comment rate, and these effects lingered for about 12 hours. Our regression analysis shows that stricter moderation and previous r/popular appearances were associated with shorter and less intense effects on the community. By quantifying the differential effects of sudden popularity, we provide recommendations for moderators to promote stability and community resilience in the face of unexpected disruptions.
Cascade-Based Randomization for Inferring Causal Effects under Diffusion Interference
Authors: Fatemi Zahra , Pouget-Abadie Jean , Zheleva Elena
Published: May 2024

The presence of interference, where the outcome of an individual may depend on the treatment assignment and behavior of neighboring nodes, can lead to biased causal effect estimation. Current approaches to network experiment design focus on limiting interference through cluster-based randomization, in which clusters are identified using graph clustering, and cluster randomization dictates the node assignment to treatment and control. However, cluster-based randomization approaches perform poorly when interference propagates in cascades, whereby the response of individuals to treatment propagates to their multi-hop neighbors. When we have knowledge of the cascade seed nodes, we can leverage this interference structure to mitigate the resulting causal effect estimation bias. With this goal, we propose a cascade-based network experiment design that initiates treatment assignment from the cascade seed node and propagates the assignment to their multi-hop neighbors to limit interference during cascade growth and thereby reduce the overall causal effect estimation error. Our extensive experiments on real-world and synthetic datasets demonstrate that our proposed framework outperforms the existing state-of-the-art approaches in estimating causal effects in network data.
Unraveling the Dynamics of Television Debates and Social Media Engagement: Insights from an Indian News Show
Authors: Garimella Kiran , Datta Abhilash
Published: May 2024

The relationship between television shows and social media has become increasingly intertwined in recent years. Social media platforms, particularly Twitter, have emerged as significant sources of public opinion and discourse on topics discussed in television shows. In India, news debates leverage the popularity of social media to promote hashtags and engage users in discussions and debates on a daily basis.

This paper focuses on the analysis of one of India's most prominent and widely-watched TV news debate shows: 'Arnab Goswami -- The Debate'. The study examines the content of the show by analyzing the hashtags used to promote it and the social media data corresponding to these hashtags. The goal is to understand the composition of the audience engaged in social media discussions related to the show.
The findings reveal that the show exhibits a strong bias towards the ruling Bharatiya Janata Party (BJP), with over 60% of the debates featuring either pro-BJP or anti-opposition content. Social media support for the show primarily comes from BJP supporters. Notably, BJP leaders and influencers play a significant role in promoting the show on social media, leveraging their existing networks and resources to artificially trend specific hashtags.
Furthermore, the study uncovers a reciprocal flow of information between the TV show and social media. We find evidence that the show's choice of topics is linked to social media posts made by party workers, suggesting a dynamic interplay between traditional media and online platforms.

By exploring the complex interaction between television debates and social media support, this study contributes to a deeper understanding of the evolving relationship between these two domains in the digital age. The findings hold implications for media researchers and practitioners, offering insights into the ways in which social media can influence traditional media and vice versa.
Search Engine Revenue from Navigational and Brand Advertising
Authors: Gleason Jeffrey , Koeninger Alice , Hu Desheng , Teurn Jessica , Bart Yakov , Knight Samsun , Robertson Ronald E. , Wilson Christo
Published: May 2024

Keyword advertising on general web search engines is a multi-billion dollar business. Keyword advertising turns contentious, however, when businesses target ads against their competitors' brand names---a practice known as "competitive poaching." To stave off poaching, companies defensively bid on ads for their own brand names. Google, in particular, has faced lawsuits and regulatory scrutiny since it altered its policies in 2004 to allow poaching.

In this study, we investigate the sources of advertising revenue earned by Google, Bing, and DuckDuckGo by examining ad impressions, clicks, and revenue on navigational and brand searches. Using logs of searches performed by a representative panel of US residents, we estimate that ads on these searches account for 28--36% of Google's search revenue, while Bing earns even more. We also find that the effectiveness of these ads for advertisers varies. We conclude by discussing the implications of our findings for advertisers and regulators.
Strategies and Attacks of Digital Militias in WhatsApp Political Groups
Authors: Kansaon Daniel , Melo Philipe de Freitas , Zannettou Savvas , Feldmann Anja , Benevenuto Fabrício
Published: May 2024

WhatsApp provides a fertile ground for the large-scale dissemination of information, particularly in countries like Brazil and India.
Given its increasing popularity and use for political discussions, it is paramount to ensure that WhatsApp groups are adequately protected from attackers who aim to disrupt the activity of WhatsApp groups.
Motivated by this, in this work, we characterize two types of attacks that may disrupt WhatsApp groups.
We look into the flooding attack, where an attacker shares a large number of usually duplicate messages within a short period, and the hijacking attack, where attackers aim to obtain complete control of the group.
We collect a large dataset of 19M messages shared in 1.6K WhatsApp public political groups from Brazil and analyze them to identify and characterize flooding and hijacking attacks.
Among other things, we find that approximately 7% of the groups receive flooding attacks, which are usually short-lived (usually less than four minutes), and groups can receive multiple flooding attacks, even within the same day.
Also, we find that most flooding attacks are executed using stickers (62% of all flooding attacks) and that, in most cases, attackers use both flooding and hijacking attacks to obtain complete control of the WhatsApp groups. Our work aims to raise user awareness about such attacks on WhatsApp and emphasizes the need to develop effective moderation tools to assist group administrators in preventing or mitigating such attacks.
SensitivAlert: Image Sensitivity Prediction in Online Social Networks Using Transformer-Based Deep Learning Models
Authors: Kqiku Lindrit , Reinhardt Delphine
Published: May 2024

Billions images are shared daily on social networks.
When shared with an inappropriate audience, user-generated images can, however, compromise users' privacy and may have severe consequences, such as dismissals. To address this issue, different solutions were proposed, ranging from graphical user interfaces to Deep Learning (DL) models to alert users based on image sensitivity prediction. Although these models show promising results, they are evaluated on datasets relying on small participants' samples.
To address this limitation, we first introduce SensitivAlert, a dataset that re-annotates the previously annotated images from two existing datasets, but using a German-speaking cohort of 907 participants.
We then leverage it to classify images according to two sensitivity classes---private or public---using recent transformer-based DL models.
In our evaluation, we first consider consensus-based generic models using our dataset as benchmark based on image content itself and its associated user tags. Moreover, we show that our fine-tuned models trained on our dataset better reflect users' image privacy conceptions.
We finally focus on individual user's privacy estimation by investigating three approaches: (1) a generic approach based on participants' consensus for fine-tuning, (2) a user-wise approach based on user's privacy preferences only, and (3) a hybrid approach that combines individual preferences with consensus-based preferences. Our results finally show that the generic and hybrid approaches outperform the user-wise one for most users, thus ensuring the feasibility of image privacy prediction preferences at the individuals' level.
Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls
Authors: Moreno José Miguel , Pastrana Sergio , Reelfs Jens Helge , Vallina Pelayo , Zannettou Savvas , Panchenko Andriy , Smaragdakis Georgios , Hohlfeld Oliver , Vallina-Rodriguez Narseo , Tapiador Juan
Published: May 2024

During the first days of the 2022 Russian invasion of Ukraine, Russia's media regulator blocked access to many global social media platforms and news sites, including Twitter, Facebook, and the BBC. To bypass the information controls set by Russian authorities, pro-Ukrainian groups explored unconventional ways to reach out to the Russian population, such as posting war-related content in the user reviews of Russian businesses available on Google Maps or Tripadvisor.
This paper provides a first analysis of this new phenomenon by analyzing the unconventional strategies used to avoid state censorship in the Russian Federation during the conflict. Specifically, we analyze reviews posted on these platforms from the beginning of the war to September 2022.
We measure the channeling of war-related messages through user reviews on Tripadvisor and Google Maps.
Our analysis of the content posted on these services reveals that users leveraged these platforms to seek and exchange humanitarian and travel advice, but also to disseminate disinformation and polarized messages. Finally, we analyze the response of platforms in terms of content moderation and their impact.
Reliability Matters: Exploring the Effect of AI Explanations on Misinformation Detection with a Warning
Authors: Seo Haeseung , Lee Sian , Lee Dongwon , Xiong Aiping
Published: May 2024

To mitigate misinformation on social media, platforms such as Facebook have offered warnings to users based on the detection results of AI systems. With the evolution of AI detection systems, efforts have been devoted to applying explainable AI (XAI) to further increase the transparency of AI decision-making. Nevertheless, few factors have been considered to understand the effectiveness of a warning with AI explanations in helping humans detect misinformation. In this study, we report the results of three online human-subject experiments (N = 2,692) investigating the framing effect and the impact of an AI system’s reliability on the effectiveness of AI warning with explanations. Our findings show that the framing effect is effective for participants’ misinformation detection, whereas the AI system’s reliability is critical for humans’ misinformation detection and participants’ trust in the AI system. However, adding the explanations can potentially increase participants’ suspicions on miss errors (i.e., false negatives) in the AI system. Furthermore, more trust is shown in the AI warning without explanations condition. We conclude by discussing the implications of our findings.
Climbing the Influence Tiers on TikTok: A Multimodal Study
Authors: Tricomi Pier Paolo , Kumar Saurabh , Conti Mauro , Subrahmanian V.S.
Published: May 2024

Corporate social media analysts break influencers into five tiers of increasing importance: Nano, Micro, Mid, Macro, and Mega. We perform a comprehensive study of TikTok influencers with two goals: (i) what factors distinguish influencers in each of these tiers from the adjacent tier(s)? (ii) of the features influencers can directly control ("actionable" features), which ones are most impactful to reach the next tier?
We build and release a novel TikTok dataset featuring over 230K videos from 5000 influencers - 1000 from each tier. The dataset includes video details such as likes, facial action units, emotions, and music information derived from Spotify. Access to the videos is facilitated through provided URLs and hydration code.
To find the most important features that distinguish influencers in a tier from those in the next tier up, we thoroughly analyze traditional features (e.g., profile information) and text, audio, and video features using statistical methods and ablation testing. Our classifiers achieve F1-scores over 80%. The most impactful actionable features are traditional and video features, including enhancing video pleasure, quality, and emphasizing facial expressions.
Finally, we collect and release a YouTube Shorts dataset to conduct a comparative analysis, aiming to identify similarities and differences between the two platforms.
Multilingual Serviceability Model for Detecting and Ranking Help Requests on Social Media during Disasters
Authors: Vitiugin Fedor , Purohit Hemant
Published: May 2024

Social media users expect quick and high-quality responses from emergency services when seeking help. However, these organizations face difficulties in detecting and prioritizing critical requests due to the overwhelming amount of information on social media and their limited human resources to tackle it during mass emergencies or disaster events. The situation is exacerbated when users communicate in different or native languages, which can be expected during disasters. While recent studies have focused on characterizing and automatically detecting help requests on social media, they focused on non-behavioral features and monolingual data, primarily in English. Thus, a key gap exists in analyzing multilingual requests on social media for public services.
In this paper, we introduce a knowledge distillation framework called MulTMR (Multiple Teachers Model for detecting and Ranking), which combines the power of both task-related and behavior-guided models as diverse teachers for training a student model to efficiently detect serviceable request messages across languages and regions on social media during natural disaster events. We demonstrate that the presented framework can enhance performance (with an AUC improvement of up to 10%) in various scenarios of multilingual test data. Our results, which were validated on real-world data collected in three languages during ten disasters across seven countries, indicate the use of behavior-guided teacher models in MulTMR can increase attention to relevant indicators of serviceability characteristics. The application of the MulTMR framework through a streaming data analytics tool could reduce the cognitive load on personnel within social media teams of emergency services. Further, its application could inform how to leverage human behavior characteristics in creating automated models for social media analytics to design systems in other public service domains beyond emergency management.
Leveraging Psychiatric Scale for Suicide Risk Detection on Social Media
Authors: Wang Bichen , Deng Pengfei , Chen Song , Zhao Yanyan , Qin Bing
Published: May 2024

The objective of suicide risk detection on social media is to identify individuals who may attempt suicide and determine their suicide risk level based on their online behavior. Although data-driven learning models have been used to predict suicide risk levels, these models often lack theoretical support and explanation from psychiatric research. To address this issue, we propose the incorporation of professional psychiatric scales into research to provide theoretical support and explanations for our model. Our proposed Scale-based Neural Network (SNN) architecture aims to extract content associated with scales from the posting history of social media users to predict their suicide risk level. Additionally, our approach provides scale-based explanations for the model's predictions. Experimental results demonstrate that our proposed method outperforms several strong baseline methods and highlights the potential of combining psychiatric scales and computational techniques to improve suicide risk detection.
Morality in the Mundane: Categorizing Moral Reasoning in Real-Life Social Situations
Authors: Xi Ruijie , Singh Munindar P.
Published: May 2024

Moral reasoning reflects how people acquire and apply moral rules in particular situations. With social interactions increasingly happening online, social media provides an unprecedented opportunity to assess in-the-wild moral reasoning. We investigate the commonsense aspects of morality empirically using data from a Reddit subcommunity (i.e., a subreddit), r/AmITheAsshole, where an author describes their behavior in a situation and seeks comments about whether that behavior was appropriate. A commenter judges and provides reasons for whether an author or others’ behaviors were wrong. We focus on the novel problem of understanding the moral reasoning implicit in user comments about the propriety of an author’s behavior. Specifically, we explore associations between the common elements of the indicated rationale and the extractable social factors. Our results suggest that a moral response depends on the author’s gender and the topic of a post. Typical situations and behaviors include expressing anger emotion and using sensible words (e.g., f-ck, hell, and damn) in work-related situations. Moreover, we find that commonly expressed reasons also depend on commenters’ interests.
Characterizing Fake News Targeting Corporations
Authors: Zhou Ke , Šćepanović Sanja , Quercia Daniele
Published: May 2024

Misinformation proliferates in the online sphere, with evident impacts on the political and social realms, influencing democratic discourse and posing risks to public health and safety. The corporate world is also a prime target for fake news dissemination. While recent studies have attempted to characterize corporate misinformation and its effects on companies, their findings often suffer from limitations due to qualitative or narrative approaches and a narrow focus on specific industries. To address this gap, we conducted an analysis utilizing social media quantitative methods and crowd-sourcing studies to investigate corporate misinformation across a diverse array of industries within the S&P 500 companies. Our study reveals that corporate misinformation encompasses topics such as products, politics, and societal issues. We discovered companies affected by fake news also get reputable news coverage but less social media attention, leading to heightened negativity in social media comments, diminished stock growth, and increased stress mentions among employee reviews. Additionally, we observe that a company is not targeted by fake news all the time, but there are particular times when a critical mass of fake news emerges. These findings hold significant implications for regulators, business leaders, and investors, emphasizing the necessity to vigilantly monitor the escalating phenomenon of corporate misinformation.
TweetIntent@Crisis: A Dataset Revealing Narratives of Both Sides in the Russia-Ukraine Crisis
Authors: Ai Lin , Gupta Sameer , Oak Shreya , Hui Zheng , Liu Zizhou , Hirschberg Julia
Published: May 2024

This paper introduces TweetIntent@Crisis, a novel Twitter dataset centered on the Russia-Ukraine crisis. Comprising over 17K tweets from government-affiliated accounts of both nations, the dataset is meticulously annotated to identify underlying intents and detailed intent-related information. Our analysis demonstrates the dataset's capability in revealing fine-grained intents and nuanced narratives within the tweets from both parties involved in the crisis. We aim for TweetIntent@Crisis to provide the research community with a valuable tool for understanding and analyzing granular media narratives and their impact in this geopolitical conflict.
GMHP7k: A Corpus of German Misogynistic Hatespeech Posts
Authors: Glasebach Jonas , Keller Max-Emanuel , Döschl Alexander , Mandl Peter
Published: May 2024

We provide a german corpus consisting of 7,061 posts authored by users of social media platforms. A group of volunteers annotated each post according to hatespeech and misogynistic/misogynous hatespeech in a binary fashion. The interrater reliability over all annotators according to Fleiss’ Kappa is 0.6409 for hatespeech and 0.8258 for misogynistic hatespeech. Furthermore, baseline measurements with machine learning based text classification with BERT are presented. Initial experiments with the corpus achieve macro average F1-scores up to 0.79 for hatespeech and 0.75 for misogynistic hatespeech. The dataset of the corpus on German Misogynistic Hatespeech Posts (GMHP7k) is publicly available.