bluuebunny/crossref_metadata_2025 huggingface dataset:
Machine Learning untuk Prediksi Kegagalan Mesin dalam Predictive Maintenance System
Authors: Hafidhoh Nisa'ul , Atmaja Ardian Prima , Syaifuddiin Gus Nanang , Sumafta Ikhwan Baidlowi , Pratama Salva Mahardhika , Khasanah Hafsah Nur
Published: May 2024

Dalam menghadapi Revolusi Industri 4.0, teknologi seperti Internet of Things, Big Data, dan Kecerdasan Buatan menjadi kunci dalam modernisasi industri. Pendekatan Machine Learning digunakan untuk memproses data multivariabel berdimensi tinggi dan mengekstrak hubungan tersembunyi dalam lingkungan industri yang kompleks. Machine Learning digunakan untuk mengklasifikasikan kegagalan mesin dalam membangun Predictive Maintenance System. Penelitian ini mengadopsi siklus CRISP-DM (Cross Industry Standard Process for Data Mining) yang terdiri dari tahap business understanding, data understanding, data preparation, modelling, evaluation dan deployment. Predictive Maintenance Dataset berupa data sintetis yang digunakan dalam penelitian ini mencerminkan situasi industri nyata terdiri dari 10.000 baris data dengan sepuluh fitur. Jenis kegagalan mesin diklasifikasikan menjadi Heat Dissipation Failure, Power Failure, Overstrain Failure, dan Tool Wear Failure. Exploratory Data Analysis dilakukan untuk mendapatkan ringkasan dan visualisasi data. Pendekatan machine learning menggunakan metode Logistic Regression dan hasil evaluasi model mencapai akurasi 96,87%, sesuai dengan kriteria sukses data. Hasil pemodelan machine learning yang dikembangkan kemudian diimplementasikan dalam aplikasi Predictive Maintenance System berbasis web untuk memudahkan pemantauan kondisi mesin dan prediksi kegagalan mesin oleh pengguna.
Accelerating String-Key Learned Index Structures via Memoization-Based Incremental Training
Authors: Kim Minsu , Hwang Jinwoo , Heo Guseul , Cho Seiyeon , Mahajan Divya , Park Jongse
Published: May 2024

Learned indexes use machine learning models to learn the mappings between keys and their corresponding positions in key-value indexes. These indexes use the mapping information as training data. Learned indexes require frequent retrainings of their models to incorporate the changes introduced by update queries. To efficiently retrain the models, existing learned index systems often harness a linear algebraic QR factorization technique that performs matrix decomposition. This factorization approach processes all key-position pairs during each retraining, resulting in compute operations that grow linearly with the total number of keys and their lengths. Consequently, the retrainings create a severe performance bottleneck, especially for variable-length string keys, while the retrainings are crucial for maintaining high prediction accuracy and in turn, ensuring low query service latency.

To address this performance problem, we develop an algorithm-hardware co-designed string-key learned index system, dubbed SIA. In designing SIA, we leverage a unique algorithmic property of the matrix decomposition-based training method. Exploiting the property, we develop a memoization-based incremental training scheme, which only requires computation over updated keys, while decomposition results of non-updated keys from previous computations can be reused. We further enhance SIA to offload a portion of this training process to an FPGA accelerator to not only relieve CPU resources for serving index queries (i.e., inference), but also accelerate the training itself. Our evaluation shows that compared to ALEX, LIPP, and SIndex, a state-of-the-art learned index systems, SIA-accelerated learned indexes offer 2.6× and 3.4× higher throughput on the two real-world benchmark suites, YCSB and Twitter cache trace, respectively.
Truss-Based Community Search over Streaming Directed Graphs
Authors: Liao Xuankun , Liu Qing , Huang Xin , Xu Jianliang
Published: May 2024

Community search aims to retrieve dense subgraphs that contain the query vertices. While many effective community models and algorithms have been proposed in the literature, none of them address the unique challenges posed by streaming graphs, where edges are continuously generated over time. In this paper, we investigate the problem of truss-based community search over streaming directed graphs. To address this problem, we first present a peeling-based algorithm that iteratively removes edges that do not meet the support constraints. To improve the efficiency of the peeling-based algorithm, we propose three optimizations that leverage the time information of the streaming graph and the structural information of trusses. As the peeling-based algorithm may suffer from inefficiency when the input peeling graph is large, we further propose a novel order-based algorithm that preserves the community by maintaining the deletion order of edges in the peeling algorithm. Extensive experimental results on real-world datasets show that our proposed algorithms outperform the baseline by up to two orders of magnitude in terms of throughput.
Detecting Metadata-Related Logic Bugs in Database Systems via Raw Database Construction
Authors: Song Jiansen , Dou Wensheng , Gao Yu , Cui Ziyu , Zheng Yingying , Wang Dong , Wang Wei , Wei Jun , Huang Tao
Published: May 2024

Database Management Systems (DBMSs) are widely used to efficiently store and retrieve data. DBMSs usually support various metadata, e.g., integrity constraints for ensuring data integrity and indexes for locating data. DBMSs can further utilize these metadata to optimize query evaluation. However, incorrect metadata-related optimizations can introduce metadata-related logic bugs, which can cause a DBMS to return an incorrect query result for a given query. In this paper, we propose a general and effective testing approach,
Raw database construction
(Radar), to detect metadata-related logic bugs in DBMSs. Given a database
db
containing some metadata, Radar first constructs a raw database
rawDb
, which wipes out the metadata in
db
and contains the same data as
db.
Since
db
and
rawDb
have the same data, they should return the same query result for a given query. Any inconsistency in their returned query results indicates a metadata-related logic bug. To effectively detect metadata-related logic bugs, we further propose a metadata-oriented testing optimization strategy to focus on testing previously unseen metadata, thus detecting more metadata-related logic bugs quickly. We implement and evaluate Radar on five widely-used DBMSs, and have detected 42 bugs, of which 38 have been confirmed as new bugs and 16 have been fixed by DBMS developers.
Rapidash: Efficient Detection of Constraint Violations
Authors: Liu Zifan , Deep Shaleen , Fariha Anna , Psallidas Fotis , Tiwari Ashish , Floratou Avrilia
Published: May 2024

Denial Constraint (DC) is a well-established formalism that captures a wide range of integrity constraints commonly encountered, including candidate keys, functional dependencies, and ordering constraints, among others. Given their significance, there has been considerable research interest in achieving fast detection of DC violations, especially to support activities related to data exploration and preparation. Despite the significant advancements in the field, prior work exhibits notable limitations when confronted with large-scale datasets: the current state-of-the-art algorithm demonstrates a quadratic (worst-case) time and space complexity relative to the dataset's number of rows. In this paper, we establish a connection between orthogonal range search and DC violation detection. We then introduce Rapidash, a novel algorithm that demonstrates near-linear time and space complexity, representing a theoretical improvement over prior work. To validate the effectiveness of our algorithm, we conduct comprehensive evaluations on both open-source and real-world production datasets, with our production datasets notably being an order of magnitude larger than the datasets employed in prior studies. Our results reveal that Rapidash achieves up to 84× faster performance compared to state-of-the-art approaches while also exhibiting superior scalability.
New database for the estimation of dynamic coefficient of friction of snow
Authors: Aggarwal Rakesh K. , Das Ranjan , Gusain Hemendra S.
Published: May 2024

Knowledge of the Coulomb dynamic coefficient of friction of snow is a vital input parameter for the estimation of run-out distance, velocity, forces, and lateral spread of the snow avalanches in the hilly regions. This parameter is defined as the ratio of the wall shear force to the normal force components of an avalanche. Avalanches are one of the most devastating natural hazards. So, the proper understanding of avalanche flow parameters is vital for the people and the infrastructure in the mountainous regions of the world. Notwithstanding the utmost significance of the Coulomb friction parameter, a few measurements are available for this parameter. In the present work, based on 32 measurements carried out from 2017 to 2020, a new database for the shear force and normal force components of the avalanches and derived values of the dynamic coefficient of friction between the chute steel surface and the flowing snow are presented. The measurements were carried out using a three-component piezoelectric load cells-based dynamometer which in turn was installed on the 12° slope of a 61-m long snow chute, located in the Pir Panjal Himalayan range of India. Based on all the measurements, the average value of the dynamic coefficient of friction for snow-steel surface is estimated to be 0.113 with a standard deviation of 0.032. The results have been exhibited to be in accordance with the published literature. It is expected that the present database will be highly significant for the validation and improvement of avalanche dynamics models especially for high-density wet snow conditions. Further, shear force and normal force components data may be useful for the designing of snow sheds in mountainous regions.
Experimental study on the influence of clay content on the shear strength of silty soil and mechanism analysis
Authors: Xu Haibiao , Li Xinming , Liu Pengpeng , Yin Song , Ren Kebin , Sun Yuzhou , Geng Jinming
Published: May 2024

Shear strengths of silty soil were determined for shallow destruction of the soil sites frequently occurring in the Central Plains area. Specimens were prepared with five different clay contents (5, 10, 15, 20, and 25%) prior to compaction at dry densities of 1.60, 1.70, and 1.80 g/cm3. Soil specimens were saturated and then the consolidated undrained shear test was conducted with eight confined pressures ranging from 1 to 400 kPa. Results indicate that the shear strength increases significantly as the clay content increases from 5 to 25%, and the cohesion c shows bilinear function with the inflection point at the clay content of 10%. The difference of cohesion in the high and low stress sections decreases gradually to almost the same value until 25% of the clay content, while the internal friction angle φ decreases with the increase in clay content. Within the range of dry density and clay content tested, the shear strength of silty soil in the low stress range obtained is higher than the measured value. Therefore, for the shallow damage of soil site, the shear strength parameters should consider the low stress test conditions. The bilinear growth of cohesion c with clay content can be attributed to the changes from “sand-like soil” to “clay-like soil” with the skeleton of soil specimen transitioning from sand particles to “clay grid” when the clay content exceeds approximately 10% combined with the results of scanning electron microscopy.
Scaling up multispectral color filters with binary lithography and reflow (BLR)
Authors: Rahman Md Abdur , Rezaei Soroosh Daqiqeh , Arora Deepshikha , Wang Hao , Mori Tomohiro , Chia Ser Chern , Chan John You En , Nair Parvathi Nair Suseela , Uddin Siam , Pan Cheng-Feng , Zhang Wang , Wang Hongtao , Ruitao Zheng , Heng Lim Sin , Yang Joel K. W.
Published: May 2024

Efforts to increase the number of filters are driven by the demand for miniaturized spectrometers and multispectral imaging. However, processes that rely on sequential fabrication of each filter are cost ineffective. Herein, we introduce an approach to produce at least 16 distinct filters based on a single low-resolution lithographic step with minimum feature size of 0.6 μm. Distinct from grayscale lithography, we employ standard binary lithography but achieve height variations in polymeric resist through a post-development reflow process. The resulting transparent polymeric films were incorporated in Fabry–Perot cavity structures with cavity thickness ranging from 90 to 230 nm to produce transmittance across the visible spectrum. This binary lithography and reflow (BLR) process demonstrates control of the dielectric layer thickness down to ∼15 nm. This new process provides a cost-effective alternative to traditional techniques in fabricating microscopic transmission filters, and other applications where precise thickness variation across the substrate is required.
Immediate effect of mud pack on resting cardiovascular parameters in hypertensive individuals – A single-arm pilot study
Authors: Sivakumar Sivaranjani , Chidambaram Yogapriya , Boopalan Deenadayalan , Arumuganainar Mangaladevi , Kathiresan Arunthathi , Narayanasamy Manavalan , Kuppusamy Maheshkumar
Published: May 2024

Background

Recent reports highlight the effectiveness of mud therapy in various health conditions. A previous study showed that applying a mud pack (MP) to the abdomen and eyes effectively lowered blood pressure (BP) in healthy individuals. However, there have been no studies investigating the immediate effects of MP on the abdomen and eyes in patients with hypertension. This pilot study aims to determine the immediate impact of MP on cardiovascular parameters in patients with hypertension.

Material and methods

This preliminary study involved 30 adults aged 50 ± 11 years who had hypertension. After obtaining detailed informed written consent from all the patients, a single session of MP application on the abdomen and eyes was administered for a duration of 20 min. BP measurements were taken both before and after the 20-min intervention.

Results

Immediately following the application of MP on the abdomen and eyes, a statistically significant (p < 0.05) reduction was observed in BP variables such as systolic BP, diastolic BP, mean arterial BP, pulse pressure, rate pressure product, and double product among the patients with hypertension.

Conclusion

The outcome of this pilot study showed that MP could be effective in reducing BP among the patients with hypertension. This suggests its potential as a safe and economically feasible adjunctive therapy for hypertension management. However, further studies with large samples are needed to find the duration of the effectiveness.
Salt Production in Central Italy and Social Network Analysis Centrality Measures: An Exploratory Approach
Authors: Fulminante Francesca , Alessandri Luca
Published: May 2024

In this work, we study salt-production settlement in central Italy with an exploratory application of centrality indexes, common in social network analysis: betweenness centrality, closeness centrality, and degree centrality. These methods are not new, but they have never been applied to this type of site and the results are innovative and illuminating. In fact, the closeness and degree centrality do not yield particularly interesting results. However, the betweenness centrality, which indicates the most commonly used routes in a given region, provide powerful insights. By indicating shifting most common routes through time, from the terrestrial and sea route along the coast in the Bronze and Iron Age, to the use of the Tiber River and Tiber valley as route, in the Orientalizing and Archaic Period, they allow us to advance hypotheses about the shift between two different productions. The briquetage salt production technique was used in the Bronze and Iron Age on the costal sites, which was also the most common route used in the region. While the proper marine production at the mouth of the Tiber, both on the Etruscan and Latin side, might develop during the Orientalizing and Archaic Age, together with an intensified use of the Via Salaria, running from the coast to the mountains of Latium, along the Tiber River. It would be interesting to confirm these hypotheses with further analyses and also targeted excavations.
Investigation on flow characteristics and its effect on heat transfer enhancement in a wedge channel with combination of circular, oblong, teardrop, and pencil pin fins
Authors: Venkatesh Goveraiahgari , Meenakshi Reddy Reddygari
Published: May 2024

Gas turbine engines are used extensively in a variety of industries, including power generation, aviation, and marine propulsion. One of the most difficult issues in developing gas turbine engines is dealing with the high temperatures. Internal cooling is a typical strategy for keeping important components, such as turbine blades, at a safe working temperature. Pin-fin cooling is a significant technology used in gas turbine blades to manage severe operating temperatures. This research paper investigates the impact of a new geometry combining two pin fins in cooling channels in gas turbine blades. The study involved testing seven different pin-fin arrays over a Reynolds number varying from 10,000 to 80,000 and a constant heat flux of 3280 W/m2 applied to all the surfaces. The findings indicate that the use of pencil pin fins leads to a notable enhancement in cooling efficiency owing to the existence of a low-pressure drop, 5.2 % compared to pencil and tear drop pin fins and by 1.6 % compared to pencil and oblong pin fins, and it is 1.7 % less than pencil and circular fins.
Adaptation Strategies: Labour Education, Climate Crisis and the UK Trade Union Movement
Authors: Tannock Stuart
Published: May 2024

A growing number of climate activists and scholars argue that an effective climate movement needs the involvement of the trade union movement, to be able to push forward the radical social transformations required to address the global climate crisis. This article analyses the recent focus on climate adaptation in labour education and action by trade unions in the UK. Climate adaptation is inherently political, and this article analyses the agendas driving the turn to adaptation, the possibilities that adaptation strategies open up, and some of their risks and limitations. Climate adaptation strategies, the article argues, could represent an important step forward for developing effective labour education and action on the climate crisis, but only if these strategies enable unions to mobilize a focus on the root causes of the crisis, agitate for structural change, and attend to the global and not just local concerns of worker, social and climate justice.
Intestinal parasite infection and sanitation in medieval Leiden, the Low Countries
Authors: Rabinow Sophie , Wang Tianyi , van Oosten Roos , Meijer Yolande , Mitchell Piers D.
Published: May 2024

In the absence of written records, disease and parasite loads are often used as indicators of sanitation in past populations. Here, the authors adopt the novel approach of integrating the bioarchaeological analysis of cesspits in an area of medieval Leiden (the Netherlands) with historical property records to explore living conditions. Using light microscopy and enzyme-linked immunosorbent assays (ELISA) they identify evidence of parasites associated with ineffective sanitation (whipworm, roundworm and the protozoan Giardia duodenalis)—at residences of all social levels—and the consumption of infected livestock and freshwater fish (Diphyllobothriidae, cf. Echinostoma sp., cf. Fasciola hepatica and Dicrocoelium sp.).
Мій шлях у житті і в науці
Authors: Комісаренко Сергій Васильович
Published: May 2024

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Adapting and Validating the Covid-19 Anxiety Scale (CAS-7) in Indonesia
Authors: Thiefanee Gilda , Kumalasari Asteria Devy
Published: May 2024

There has been a lack of anxiety assessments designed explicitly in the context of COVID-19, particularly screening tools that are easy to understand for diverse demographics and easily administered to accommodate Indonesia’s large population. CAS-7 has proven to be an effectively administrable, reliable, and valid assessment to measure pandemic‑related anxiety among the Indian population. This study aimed to adapt and validate CAS-7 to provide a reliable and valid screening tool to timely identify citizens of Indonesia suffering from pandemic-related anxiety and assign them suitable interventions. There were 1,121 subjects from 29 provinces participated in the study. Factorial validity was tested with Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) in JASP 15.0.0. while convergent validity, test-retest reliability, and internal reliability were tested with Pearson product-moment correlation coefficient, intraclass correlation coefficient, and Cronbach’s alpha in SPSS 25. The adaptation of CAS-7 has proven to be a satisfactory, reliable, and valid screening tool for identifying citizens of Indonesia suffering from pandemic-related anxiety, with changes made in item arrangement and factor interpretation.
Coding Hotline
Authors:   American Academy of Pediatrics
Published: May 2024

This month’s Coding Hotline addresses diagnosis coding for a newborn with a fractured clavicle, depression screening, and medical decision-making.
Subjective ibogaine experiences across intersecting social-ecological dimensions
Authors: Ozmat Evan E. , McDonough Alicia K. , Ladouceur Guy M. , Roy Darin S. , Bozek Dana M. , Oh Junsung , Martin Jessica L.
Published: May 2024

Background and aims

Ibogaine, an alkaloid extracted from the root of the Tabernanthe Iboga shrub, holds promise in treating addictive disorders. Individuals receiving ibogaine treatment report decreased withdrawal symptoms, cravings, and addiction severity. These changes are often attributed to ibogaine's psychedelic properties: subjectively meaningful, dream-like experiences. Research demonstrates that ibogaine experiences impact multiple social-ecological dimensions and influence addiction and recovery trajectories. No studies have explored the multidimensional experiences of individuals seeking ibogaine treatment for addictions. The current study examines acute and enduring ibogaine experiences at individual, interpersonal, and behavioral health systems levels.

Methods

Fifteen individuals (5 female, 10 male) who received ibogaine treatment at a medical clinic in Ensenada, Mexico completed semi-structured interviews asking about individual, interpersonal, and behavioral health system experiences post-ibogaine within 12 months of ibogaine treatment (Mdn = 169 days). Transcripts were coded and analyzed using a Consensual Qualitative Research approach.

Results

Many participants reported seeking ibogaine treatment after exhausting other addiction services. Outcomes reported following treatment included alleviated cravings and withdrawal symptoms. Participants indicated that ibogaine experiences prompted insight into their addictions and contributed to their recovery. Self-support and disclosing ibogaine experiences to professional and non-professional supporters were important to participants' recovery post-ibogaine.

Conclusions

Intersecting experiences across social-ecological levels had an enduring impact on participants' addiction recovery after ibogaine treatment. Despite negative experiences with behavioral health systems prior to receiving ibogaine treatment, most participants sought the support of mental health professionals post-ibogaine. Findings are informative for mental health professionals and clients interested in ibogaine treatment.
Anna Hickey-Moody, Faith Stories: Sustaining Meaning and Community in Troubling Times
Authors: Cuperus Jerrold
Published: May 2024

Anna Hickey-Moody, Faith Stories: Sustaining Meaning and Community in Troubling Times. Manchester: Manchester University Press, 2023, pp. 240, ISBN: 978-1-5261-6524-4. £25.00 [Open Access].
The Story of the Church Warden V.I. Volokitin, who Loved the Soviet Government – without Reciprocity
Authors: Alexander Kazankov
Published: May 2024

The historical analysis of the daily religiosity of the West Ural peasantry in the first half of the twentieth century remains an urgent research task. Of particular importance to such a subject is the fact that at the turn of the 1920s – 1930s, the village experienced one of the most extensive social transformations which began with a campaign for total collectivization. For the peasants involved in this process, religiosity (and the type of everyday mentality in general) turned out to be a factor that sometimes determined the nature of practical actions and the fate of a person. Having set out to reconstruct the peasant everyday life of a bygone era, the researcher will certainly face the problem of a shortage of authentic evidence coming from the environment of interest to him. The article deals with methodological and source-based problems related to the study of everyday village life in the early Soviet era. The possibility of using documents stored in archival and investigative files as sources of personal origin is substantiated. On the basis of an application to the NKVD, the daily life of an individual peasant V.I. Volokitin from 1917 to 1935 is restored. Vasily Ivanovich Volokitin, a disabled veteran of the First World War who had awards and a pension for injury and concussion, took part in the establishment of Soviet power in the Kama region. His family was subjected to repression at a time when the Kungursky and Osinsky districts were occupied by units of the Siberian Army. He considered the Soviet government his own and strenuously demonstrated loyalty in a statement addressed to the operative V.A. Kushkin. But, being a religious man and a church warden, V.I. Volokitin avoided joining the collective farm. He hoped that the modest household of a disabled individual peasant would not attract the attention of the authorities. As a result, his property was siezed and sold off, and he himself was under investigation as a participant in a fictitious "church-monarchist organization." The analysis of V.I. Volokitin's biography allows us to see the characteristic patterns of peasant mentality, as well as the action of mechanisms of power coercion observed "from below".
Pathological aspects of a septicemic salmonellosis outbreak caused by Salmonella serotype Typhimurium in captive blue-fronted Amazon parrots (Amazona aestiva)
Authors: Campos Jecelen A. , Guizelini Carolina C. , Pavarini Saulo P. , Azuaga Lucas B.S. , Souza Michelli L. , Leal Cássia R.B. , Ramos Carlos A.N. , Gomes Danilo C.
Published: May 2024

ABSTRACT: There are reports of septicemic salmonellosis in exotic birds caused by Salmonella enterica serotype Typhimurium, although they are scarce and do not address the anatomopathological and immunohistochemical aspects of this condition in blue-fronted Amazon parrots (Amazona aestiva). This study aims to report an outbreak of salmonellosis by S. Typhimurium in 15 blue-fronted parrots introduced to a rehabilitation center in the state of Mato Grosso do Sul, Brazil. Hepatomegaly and splenomegaly, accompanied by flat white areas in the capsule and parenchyma, were the most frequent gross changes, histologically represented by necrotic and heterophilic hepatitis and splenitis and associated with bacteria in the cytoplasm of macrophages and free in the affected tissue. S. Typhimurium was identified from bacterial culture, PCR, and DNA sequencing. Along with immunohistochemistry samples of liver, spleen, kidney, small intestine, pancreas, thyroid, heart, and lung, immunostaining was used for Salmonella spp. without previous bacterial isolation. The lethality of this outbreak demonstrates the relevance of salmonellosis in blue-fronted parrots, which are known to be carriers of this bacteria.
UMA PSICANÁLISE NEGRA: DESOBEDIÊNCIAS ONTOLÓGICAS E EPISTÊMICAS DE PSICÓLOGAS E PSICANALISTAS RACIALIZADAS
Authors: Sousa-Duarte Fernanda , Morais Ana Paula
Published: May 2024

Resumo: A partir de uma perspectiva decolonial, o objetivo da pesquisa foi compreender os lugares do negro na Psicanálise atualmente. Para isto, foram realizadas 13 entrevistas semiestruturadas com psicólogas e psicanalistas negras com prática clínica. A partir de análise categorial-temática, identificamos quatro categorias: escolha da Psicanálise, articulações entre Psicanálise e relações raciais, descobrindo-se negra e a cor da Psicanálise e das (não) psicanalistas. Diante do reconhecimento das limitações da Psicanálise, foi a experiência vivida do negro o motor de deslocamento ontológico que viabilizou desobediências epistêmicas configuradas na articulação da Psicanálise com teorias sociais. Este resultado evidencia o entrelaçamento da colonialidade do saber e do ser na matriz colonial do poder e enfatiza a desobediência ontológica como via fundamental para práticas psis antirracistas.
Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts
Authors: Burghardt Keith , Rao Ashwin , Chochlakis Georgios , Sabyasachee Baruah , Guo Siyi , He Zihao , Rojecki Andrew , Narayanan Shrikanth , Lerman Kristina
Published: May 2024

Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are not well understood. We analyze a five million-tweet multilingual dataset related to the 2017 French presidential election, when a major information campaign led by Russia called "#MacronLeaks" took place. We utilize heuristics to identify coordinated inauthentic accounts and detect attitudes, concerns and emotions within their tweets, collectively known as socio-linguistic characteristics. We find that coordinated accounts retweet other coordinated accounts far more than expected by chance, while being exceptionally active just before the second round of voting. Concurrently, socio-linguistic characteristics reveal that coordinated accounts share tweets promoting a candidate at three times the rate of non-coordinated accounts. Coordinated account tactics also varied in time to reflect news events and rounds of voting. Our analysis highlights the utility of socio-linguistic characteristics to inform researchers about tactics of coordinated accounts and how these may feed into online social manipulation.
“I Am 30F and Need Advice!”: A Mixed-Method Analysis of the Effects of Advice-Seekers’ Self-Disclosure on Received Replies
Authors: Chen Yixin , Hale Scott , Hogan Bernie
Published: May 2024

In community question answering sites, users can easily make a post to ask questions or seek advice. Others volunteer replies to these posts to provide answers of varying quality, detail, and helpfulness. In the advice-seeking process, self-disclosure enables posters to provide a relatable context for their requests but comes at a cost of greater identifiability. We focus on the "r/Advice" Reddit community and present a mixed-method study on how self-disclosure of advice-seekers shapes the prevalence and detail of the feedback received. We focus particularly on age and gender disclosure as both are reliably detected and normatively considered in the context of giving advice. We use both hurdle negative binomial regression models and discourse analysis to examine the relationship between self-disclosure and the replies received and explore themes related to disclosure. The results show that advice-seekers' age or gender disclosure correlates with more replies and more helpful replies, but the effects of age and gender disclosure are not additive. We also find both reciprocity and homophily effects in disclosure as reply-givers are more likely to self-disclose when the advice-seeker does so. The lack of additive effects alongside the thematic analysis suggests disclosure practices are used to elicit sufficient credibility or basis for empathy, whereas too much or too little disclosure creates uncertainty or inhibits the applicability of the received advice.
#LetsTalk: Understanding Social Media Usage of Self-Harm Users in India
Authors: Chhikara Garima , Chakraborty Abhijnan
Published: May 2024

Mental health concerns, such as depression, pose significant challenges for support systems in effectively identifying affected individuals. On the other hand, people suffering from depression often find it easier to discuss on social media rather than in face-to-face interactions. Additionally, the development of distressing conditions typically arises from a multitude of factors accumulated over time rather than a singular event. To gain a fine-grained understanding of these facets, in this work, we perform a longitudinal analysis of the tweeting behaviour of Indian users who post content related to self-harm. We categorise users based on their posting frequency and examine various aspects including their social network, bio descriptions, tweeting preferences, temporal variations and cognitive indicators. By elucidating these nuances, we aim to contribute insights that could aid in the early detection of mental health issues and prompt timely intervention from support networks.
Emergent Influence Networks in Good-Faith Online Discussions
Authors: Dambanemuya Henry Kudzanai , Romero Daniel , Horvát Emőke-Ágnes
Published: May 2024

Town hall-type debates are increasingly moving online, irrevocably transforming public discourse. Yet, we know relatively little about crucial social dynamics that determine which arguments are more likely to be successful. This study investigates the impact of one's position in the discussion network created via responses to others' arguments on one's persuasiveness in unfacilitated online debates. We propose a novel framework for measuring the relationship between network position and persuasiveness, using a combination of social network analysis and machine learning. Complementing existing studies investigating the effect of linguistic aspects on persuasiveness, we show that the user's position in a discussion network is associated with their persuasiveness online. Moreover, the recognition of successful persuasion is linked to an increase in dominant network position. Our findings offer important insights into the complex social dynamics of online discourse and provide practical insights for organizations and individuals seeking to understand the interplay between influential positions in a discussion network and persuasive strategies in digital spaces.
Stories That Heal: Characterizing and Supporting Narrative for Suicide Bereavement
Authors: Doyle Dylan Thomas , Ghosh Jay K. , Suchocki Reece , Keegan Brian C. , Voida Stephen , Brubaker Jed R.
Published: May 2024

Clinical group bereavement therapy often promotes narrative sharing as a therapeutic intervention to facilitate grief processing. Increasingly, people turn to social media to express stories of loss and seek support surrounding bereavement experiences, specifically, the loss of loved ones from suicide. This paper reports the results of a computational linguistic analysis of narrative expression within an online suicide bereavement support community. We identify distinctive characteristics of narrative posts (compared to non-narrative posts) in linguistic style. We then develop and validate a machine-learning model for tagging narrative posts at scale and demonstrate the utility of applying this machine-learning model to a more general grief support community. Through comparison, we validate our model's narrative tagging accuracy and compare the proportion of narrative posts between the two communities we have analyzed. Narrative posts make up about half of all total posts in these two grief communities, demonstrating the importance of narrative posts to grief support online. Finally, we consider how the narrative tagging tool presented in this study can be applied to platform design to more effectively support people expressing the narrative sharing of grief in online grief support spaces.
#TeamFollowBack: Detection & Analysis of Follow Back Accounts on Social Media
Authors: Elmas Tuğrulcan , Randl Mathis , Attia Youssef
Published: May 2024

Follow back accounts inflate their follower counts by engaging in reciprocal followings. Such accounts manipulate the public and the algorithms by appearing more popular than they really are. Despite their potential harm, no studies have analyzed such accounts at scale. In this study, we present the first large-scale analysis of follow back accounts. We formally define follow back accounts and employ a honeypot approach to collect a dataset of such accounts on X (formerly Twitter). We discover and describe 12 communities of follow back accounts from 12 different countries, some of which exhibit clear political agenda. We analyze the characteristics of follow back accounts and report that they are newer, more engaging, and have more followings and followers. Finally, we propose a classifier for such accounts and report that models employing profile metadata and the ego network have some success, although achieving high recall is challenging. Our study enhances understanding of the follow back accounts and discovering such accounts in the wild.
The Evolution of Occupational Identity in Twitter Biographies
Authors: Guo Xingzhi , Handzlik Dakota , Jones Jason J. , Skiena Steven S.
Published: May 2024

Occupational identity concerns the self-image of an individual’s affinities and socioeconomic class, and directs how a person should behave in certain ways. Understanding the establishment of occupational identity is important to study
work-related behaviors. However, large-scale quantitative studies of occupational identity are difficult to perform due to its indirect observable nature. But profile biographies on social media contain concise yet rich descriptions about self- identity. Analysis of these self-descriptions provides powerful insights concerning how people see themselves and how they change over time.
In this paper, we present and analyze a longitudinal corpus recording the self-authored public biographies of 51.18 million Twitter users as they evolve over a six-year period from 2015-2021. In particular, we investigate the social approval (e.g., job prestige and salary) effects in how people self-disclose occupational identities, quantifying over-represented occupations as well as the occupational transitions w.r.t. job prestige over time. We show that self-reported jobs and job transitions are biased toward more prestigious occupations. We also present an intriguing case study about how self-reported jobs changed amid COVID-19 and the subsequent "Great Resignation" trend with the latest full year data in 2022. These results demonstrate that social media biographies are a rich source of data for quantitative social science studies, allowing unobtrusive observation of the intersections
and transitions obtained in online self-presentation.
Leveraging Recommender Systems to Reduce Content Gaps on Peer Production Platforms
Authors: Houtti Mo , Johnson Isaac , Warncke-Wang Morten , Terveen Loren
Published: May 2024

Peer production platforms like Wikipedia commonly suffer from content gaps. Prior research suggests recommender systems can help solve this problem, by guiding editors towards underrepresented topics. However, it remains unclear whether this approach would result in less relevant recommendations, leading to reduced overall engagement with recommended items. To answer this question, we first conducted offline analyses (Study 1) on SuggestBot, a task-routing recommender system for Wikipedia, then did a three-month controlled experiment (Study 2). Our results show that presenting users with articles from underrepresented topics increased the proportion of work done on those articles without significantly reducing overall recommendation uptake. We discuss the implications of our results, including how ignoring the article discovery process can artificially narrow recommendations on peer production platforms.
Tight Sampling in Unbounded Networks
Authors: Jaglan Kshitijaa , Pindiprolu Meher Chaitanya , Sharma Triansh , Singam Abhijeeth Reddy , Goyal Nidhi , Kumaraguru Ponnurangam , Brandes Ulrik
Published: May 2024

The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnetworks will crucially depend on the sampling scheme. Motivated by studies of homophily and opinion formation, we propose a variant of snowball sampling designed to prioritize the inclusion of entire cohesive communities rather than any kind of representativeness, breadth, or depth of coverage. The method is illustrated on a concrete example, and experiments on synthetic networks suggest that it behaves as desired.
Assessing the Impact of Online Harassment on Youth Mental Health in Private Networked Spaces
Authors: Kim Seunghyun , Razi Afsaneh , Alsoubai Ashwaq , Wisniewski Pamela J. , De Choudhury Munmun
Published: May 2024

Online harassment negatively impacts mental health, with victims expressing increased concerns such as depression, anxiety, and even increased risk of suicide, especially among youth and young adults. Yet, research has mainly focused on building automated systems to detect harassment incidents based on publicly available social media trace data, overlooking the impact of these negative events on the victims, especially in private channels of communication. Looking to close this gap, we examine a large dataset of private message conversations from Instagram shared and annotated by youth aged 13-21. We apply trained classifiers from online mental health to analyze the impact of online harassment on indicators pertinent to mental health expressions. Through a robust causal inference design involving a difference-in-differences analysis, we show that harassment results in greater expression of mental health concerns in victims up to 14 days following the incidents, while controlling for time, seasonality, and topic of conversation. Our study provides new benchmarks to quantify how victims perceive online harassment in the immediate aftermath of when it occurs. We make social justice-centered design recommendations to support harassment victims in private networked spaces. We caution that some of the paper's content could be triggering to readers.
Throw Your Hat in the Ring (of Wikipedia): Exploring Urban-Rural Disparities in Local Politicians' Information Supply
Authors: Matsui Akira , Miyazaki Kunihiro , Murayama Taichi
Published: May 2024

In this era of digital politics, understanding the factors that influence the supply of political information is important. This study investigates the relationship between socio-economic status and the political information supplied on Wikipedia. To this end, it employs a dataset of politicians who ran for local elections in Japan over approximately 20 years and discovers that the creation and revisions of local politicians' pages are associated with socio-economic factors such as the employment ratio by industry and age distribution. We find that the majority of the suppliers of politicians' information are unregistered and primarily interested in politicians' pages compared to registered users. Additional analysis reveals that users who supply information about politicians before and after an election are more active on Wikipedia than the average user. The findings presented imply that the information supply on Wikipedia, which relies on voluntary contributions, may reflect regional socio-economic disparities.
Wildlife Product Trading in Online Social Networks: A Case Study on Ivory-Related Product Sales Promotion Posts
Authors: Mou Guanyi , Yue Yun , Lee Kyumin , Zhang Ziming
Published: May 2024

Wildlife trafficking (WLT) has evolved into a pressing global concern, as traffickers increasingly utilize online platforms such as e-commerce websites and social networks to expand their illicit trade. This paper addresses the pivotal challenge of detecting and recognizing promotional behaviors related to the sale of wildlife products within online social networks—a critical step in combating these environmentally detrimental activities. To confront these illicit operations effectively, our research undertakes the following key initiatives: 1. Data Collection and Labeling: We employ a network-based approach to gather a scalable dataset pertaining to wildlife product trading. Through a human-in-the-loop machine learning process, this dataset is meticulously labeled, distinguishing between positive class samples containing wildlife product selling posts and hard-negatives representing regular posts misclassified as potential WLT posts, subsequently rectified by human annotators. 2. Machine Learning Framework Development: We present a robust framework that benchmarks machine learning results on the collected dataset. This framework autonomously identifies suspicious wildlife selling posts and accounts, effectively harnessing the multi-modal nature of online social networks. 3. In-depth Analysis of Trading Behaviors: Our research delves into a comprehensive analysis of trading posts, illuminating the systematic and organized selling behaviors prevalent in the current landscape. By providing detailed insights into the nature of these behaviors, we contribute valuable information for understanding and countering illegal wildlife product trading. Moreover, we emphasize our commitment to openness and collaboration by making our code and dataset openly available, thereby fostering cooperative efforts towards the development of more effective strategies in combating illegal wildlife trafficking.
A Domain Adaptive Graph Learning Framework to Early Detection of Emergent Healthcare Misinformation on Social Media
Authors: Shang Lanyu , Zhang Yang , Yue Zhenrui , Choi YeonJung , Zeng Huimin , Wang Dong
Published: May 2024

A fundamental issue in healthcare misinformation detection is the lack of timely resources (e.g., medical knowledge, annotated data), making it challenging to accurately detect emergent healthcare misinformation at an early stage. In this paper, we develop a crowdsourcing-based early healthcare misinformation detection framework that jointly exploits the medical expertise of expert crowd workers and adapts the medical knowledge from a source domain (e.g., COVID-19) to detect misleading posts in an emergent target domain (e.g., Mpox, Polio). Two important challenges exist in developing our solution: (i) How to leverage the complex and noisy knowledge from the source domain to facilitate the detection of misinformation in the target domain? (ii) How to effectively utilize the limited amount of expert workers to correct the inapplicable knowledge facts in the source domain and adapt the corrected facts to examine the truthfulness of the posts in the emergent target domain? To address these challenges, we develop CrowdAdapt, a crowdsourcing-based domain adaptive approach that effectively identifies and adapts relevant knowledge facts from the source domain to accurately detect misinformation in the target domain. Evaluation results from two real-world case studies demonstrate the superiority of CrowdAdapt over state-of-the-art baselines in accurately detecting emergent healthcare misinformation.