bluuebunny/crossref_metadata_2025 huggingface dataset:
Why Do You Write This? Prediction of Influencers from Word Use
Authors: Mahmud Jalal
Published: November 2022

With the widespread usage of social media, there has been much effort on detecting influential users for different in-formation propagation applications. An inherent limitation of such methods is that they can only detect influential users after such users show observable signals of influence. How-ever, in many real world applications including a counter-campaign, an organization needs a way to detect influencers early, so that they can take appropriate measure before it is too late to intervene. In this work, we present a method to detect such would-be influencers from their prior word us-age in social media. We compute psycholinguistic category scores from word usage, and investigate how people with different scores exhibited different influence behaviors on Twitter. We also found psycholinguistic categories that show significant correlations with such behaviors, and built predictive models of influence from such category based features. Our experiments using a real world dataset vali-dates that such predictions can be done with reasonable ac-curacy.
School Bullying in Twitter and Weibo: A Comparative Study
Authors: Xu Jun-Ming , Huang Hsun-Chih , Bellmore Amy , Zhu Xiaojin
Published: November 2022

School-based bullying is a serious health issue among adolescents world wide. We identify several differences in microblogs of school-based bullying between Twitter (mostly representing the USA) and Weibo (mostly representing China). First, we see a smaller fraction of victim authors in Weibo than in Twitter. We hypothesize that this may be due to Asian culture's emphasis on saving face where it is more of a taboo to be a victim or label someone a victim. Second, we see different temporal dynamics of school bullying posts due to differences in holidays and length of school days. Finally, bullying posts from Weibo contain more mentions of family than those from Twitter. This may be due to the greater emphasis on family in Asian cultures.
Topic-Based Clusters in Egocentric Networks on Facebook
Authors: Weng Lilian , Lento Thomas
Published: November 2022

Homophily suggests that people tend to befriend others with shared traits, such as similar topical interests or overlapping social circles. We study how people communicate online in term of conversation topics from an egocentric viewpoint using a dataset from Facebook. We find that friends who favor similar topics form topic-based clusters; these clusters have dense connectivities, large growth rates, and little overlap.
Measure Social Metrics with Sodatech: a Monitoring and Analysis Platform of Big Data
Authors: Li Yingmin , Balagué Christine
Published: November 2022

Social Network Services (SNS) nowadays require marketing research study using Big Data tools. In this paper we present Sodatech platform: a monitoring and analysis tool of big data to help researchers and SNS community managers to measure effectiveness of social media marketing activities. Sodatech platform provides three functions: collect massive data from social network; analyze marketing metrics with big data tools; display intuitive data in a dynamic and interactive interface.
Characterizing Silent Users in Social Media Communities
Authors: Gong Wei , Lim Ee-Peng , Zhu Feida
Published: November 2022

Silent users often constitute a significant proportion of an online user-generated content system. In the context of social media such as Twitter, users can opt to be silent all or most of the time. They are often called the invisible participants or lurkers. As lurkers contribute little to the online content, existing analysis often overlooks their presence and voices. However, we argue that understanding lurkers is important in many applications such as recommender systems, targeted advertising, and social sensing. This research therefore seeks to characterize lurkers in social media and propose methods to profile them. We examine 18 weeks of tweets generated by two Twitter communities consisting of more than 110K and 114K users respectively. We find that there are many lurkers in the two communities, and the proportion of lurkers in each community changes with time.We also show that by leveraging lurkers' neighbor content, we are able to profile them with accuracy comparable to that of profiling active users. It suggests that user generated content can be utilized for profiling lurkers and lurkers in Twitter are after all not that ``invisible''.
A Bayesian Graphical Model to Discover Latent Events from Twitter
Authors: Wei Wei , Joseph Kenneth , Lo Wei , Carley Kathleen
Published: November 2022

Online social networks like Twitter and Facebook produce an overwhelming amount of information every day. However, research suggests that much of this content focuses on a reasonably sized set of ongoing events or topics that are both temporally and geographically situated. These patterns are especially observable when the data that is generated contains geospatial information, usually generated by a location enabled device such as a smartphone. In this paper, we consider a data set of 1.4 million geo-tagged tweets from a country during a large social movement, where social events and demonstrations occurred frequently. We use a probabilistic graphical model to discover these events within the data in a way that informs us of their spatial, temporal and topical focus. Quantitative analysis suggests that the streaming algorithm proposed in the paper uncovers both well-known events and lesser-known but important events that occurred within the timeframe of the dataset. In addition, the model can be used to predict the location and time of texts that do not have these pieces of information, which accounts for the much of the data on the web.
Values in Words: Using Language to Evaluate and Understand Personal Values
Authors: Boyd Ryan , Wilson Steven , Pennebaker James , Kosinski Michal , Stillwell David , Mihalcea Rada
Published: November 2022

People's values provide a decision-making framework that helps guide their everyday actions. Most popular methods of assessing values show tenuous relationships with everyday behaviors. Using a new Amazon Mechanical Turk dataset (N = 767) consisting of people's language, values, and behaviors, we explore the degree to which attaining "ground truth" is possible with regards to such complicated mental phenomena. We then apply our findings to a corpus of Facebook user (N=130,828) status updates in order to understand how core values influence the personal thoughts and behaviors that users share through social media. Our findings suggest that self-report questionnaires for abstract and complex phenomena, such as values, are inadequate for painting an accurate picture of individual mental life. Free response language data and language modeling show greater promise for understanding both the structure and content of concepts such as values and, additionally, exhibit a predictive edge over self-report questionnaires.
Hierarchical Estimation Framework of Multi-Label Classifying: A Case of Tweets Classifying into Real Life Aspects
Authors: Yamamoto Shuhei , Satoh Tetsuji
Published: November 2022

Many people share their daily events and opinions on Twitter. Some are beneficial and comment on several aspects of a user’s real life, i.e., eating, traffic conditions, weather, and so on. Since some tweets indicate two or more aspects, multi-label classification is required. Typical methods are not performed on tweets because they consist of short and elided sentences. To conquer these problems, we are researching a hierarchical estimation framweork (HEF) to estimate several aspects of unknown tweets. HEF is composed of both unsupervised and supervised machine learnings. In the first phase, it extracts topics from a sea of tweets using latent dirichlet allocation (LDA). In the second phase, it calculates the relevance between topcis and aspects using a small set of labeled tweets to build associations among them. In this paper, we introduce the entropy feedback method in the second phase. We evaluate the Shannon entropy of each association between the aspects and topics and iteratively calculate the feedback coefficients by entropy to achieve optimal associations. Our sophisticated experimental evaluations with a large amount of actual tweets demonstrate the high efficiency of our multi-labeling method. Our entropy feedback method successfully increased higher F-measures in all aspects. Expecially in Disaster and Traffic aspects, precision greatly increased without decreasing recall.
Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks
Authors: Rossi Luca , Williams Matthew , Stich Christoph , Musolesi Mirco
Published: November 2022

With the advent of GPS enabled smartphones, an increasing number of users is actively sharing their location through a variety of applications and services. Along with the continuing growth of Location-Based Social Networks (LBSNs), security experts have increasingly warned the public of the dangers of exposing sensitive information such as personal location data. Most importantly, in addition to the geographical coordinates of the user's location, LBSNs allow easy access to an additional set of characteristics of that location, such as the venue type or popularity. In this paper, we investigate the role of location semantics in the identification of LBSN users. We simulate a scenario in which the attacker's goal is to reveal the identity of a set of LBSN users by observing their check-in activity. We then propose to answer the following question: what are the types of venues that a malicious user has to monitor to maximize the probability of success? Conversely, when should a user decide whether to make his/her check-in to a location public or not? We perform our study on more than 1 million check-ins distributed over 17 urban regions of the United States. Our analysis shows that different types of venues display different discriminative power in terms of user identity, with most of the venues in the "Residence" category providing the highest re-identification success across the urban regions. Interestingly, we also find that users with a high entropy of their check-ins distribution are not necessarily the hardest to identify, suggesting that it is the collective behaviour of the users' population that determines the complexity of the identification task, rather than the individual behaviour.
Don’t Let Me Be #Misunderstood: Linguistically Motivated Algorithm for Predicting the Popularity of Textual Memes
Authors: Tsur Oren , Rappoport Ari
Published: November 2022

Prediction of the popularity of online textual snippets gained much attention in recent years. In this paper we investigate some of the factors that contribute to popularity of specific phrases such as Twitter hashtags. We define a new prediction task and propose a linguistically motivated algorithm for accurate prediction of hashtag popularity. Our prediction algorithm successfully models the interplay between various constraints such as the length restriction, typing effort and ease of comprehension. Controlling for network structure and social aspects we get a glimpse into the processes that shape the way we produce language and coin new words. In order to learn the interactions between the constraints we cast the problem as a ranking task. We adapt Gradient Boosted Trees for learning ranking functions in order to predict the hashtags/neologisms to be accepted. Our results outperform several baseline algorithms including SVM-rank, while maintaining higher interpretability, thus our model's prediction power can be used for better crafting of future hashtags.
Predicting Speech Acts in MOOC Forum Posts
Authors: Arguello Jaime , Shaffer Kyle
Published: November 2022

Students in a Massive Open Online Course (MOOC) interact with each other and the course staff through online discussion forums. While discussion forums play a central role in MOOCs, they also pose a challenge for instructors. The large number of student posts makes it difficult for an instructor to know where to intervene to answer questions, resolve issues, and provide feedback. In this work, we focus on automatically predicting speech acts in MOOC forum posts. Our speech act categories describe the purpose or function of the post in the ongoing discussion. Specifically, we address three main research questions. First, we investigate whether crowdsourced workers can reliably label MOOC forum posts using our speech act definitions. Second, we investigate whether our speech acts can help predict instructor interventions and assignment completion and performance. Finally, we investigate which types of features (derived from the post content, author, and surrounding context) are most effective for predicting our different speech act categories.
On the k-Anonymization of Time-Varying and Multi-Layer Social Graphs
Authors: Rossi Luca , Musolesi Mirco , Torsello Andrea
Published: November 2022

The popularity of online social media platforms provides an unprecedented opportunity to study real-world complex networks of interactions. However, releasing this data to researchers and the public comes at the cost of potentially exposing private and sensitive user information. It has been shown that a naive anonymization of a network by removing the identity of the nodes is not sufficient to preserve users' privacy. In order to deal with malicious attacks, k-anonymity solutions have been proposed to partially obfuscate topological information that can be used to infer nodes' identity. In this paper, we study the problem of ensuring k-anonymity in time-varying graphs, i.e., graphs with a structure that changes over time, and multi-layer graphs, i.e., graphs with multiple types of links. More specifically, we examine the case in which the attacker has access to the degree of the nodes. The goal is to generate a new graph where, given the degree of a node in each (temporal) layer of the graph, such a node remains indistinguishable from other k-1 nodes in the graph. In order to achieve this, we find the optimal partitioning of the graph nodes such that the cost of anonymizing the degree information within each group is minimum. We show that this reduces to a special case of a Generalized Assignment Problem, and we propose a simple yet effective algorithm to solve it. Finally, we introduce an iterated linear programming approach to enforce the realizability of the anonymized degree sequences. The efficacy of the method is assessed through an extensive set of experiments on synthetic and real-world graphs.
Coordination and Efficiency in Decentralized Collaboration
Authors: Romero Daniel , Huttenlocher Dan , Kleinberg Jon
Published: November 2022

Environments for decentralized on-line collaboration are now widespread on the Web, underpinning open-source efforts, knowledge creation sites including Wikipedia, and other experiments in joint production. When a distributed group works together in such a setting, the mechanisms they use for coordination can play an important role in the effectiveness of the group’s performance. Here we consider the trade-offs inherent in coordination in these on-line settings, balancing the benefits to collaboration with the cost in effort that could be spent in other ways. We consider two diverse domains that each contain a wide range of collaborations taking place simultaneously — Wikipedia and GitHub — allowing us to study how coordination varies across different projects. We analyze trade-offs in coordina- tion along two main dimensions, finding similar effects in both our domains of study: first we show that, in aggregate, high-status projects on these sites manage the coordination trade-off at a different level than typical projects; and second, we show that projects use a different balance of coordination when they are “crowded,” with relatively small size but many participants. We also develop a stylized theoretical model for the cost-benefit trade-off inherent in coordination and show that it qualitatively matches the trade-offs we observe between crowdedness and coordination.
The Lifecyle of a Youtube Video: Phases, Content and Popularity
Authors: Yu Honglin , Xie Lexing , Sanner Scott
Published: November 2022

This paper proposes a new representation to explain and predict popularity evolution in social media. Recent work on social networks has led to insights about the popularity of a digital item. For example, both the content and the network matters, and gaining early popularity is critical. However, these observations did not paint a full picture of popularity evolution; some open questions include: what kind of popularity trends exist among different types of videos, and will an unpopular video become popular? To this end, we propose a novel phase representation that extends the well-known endogenous growth and exogenous shock model (Crane and Sornette 2008). We further propose efficient algorithms to simultaneously estimate and segment power-law shaped phases from historical popularity data. With the extracted phases, we found that videos go through not one, but multiple stages of popularity increase or decrease over many months. On a dataset containing the 2-year history of over 172,000 YouTube videos, we observe that phases are directly related to content type and popularity change, e.g., nearly 3/4 of the top 5% popular videos have 3 or more phases, more than 60% news videos are dominated by one long power-law decay, and 75% of videos that made a significant jump to become the most popular videos have been in increasing phases. Finally, we leverage this phase representation to predict future viewcount gain and found that using phase information reduces the average prediction error over the state-of-the-art for videos of all phase shapes.
Project Recommendation Using Heterogeneous Traits in Crowdfunding
Authors: Rakesh Vineeth , Choo Jaegul , Reddy Chandan K.
Published: November 2022

Crowdfunding has gained widespread popularity in recent years. By funding entrepreneurs with creative minds, it is gradually taking over the role of venture capitalists who provide the much needed seed capital to jump start business ventures. Despite the huge success of the crowdfunding platforms, not every project is successful in reaching its funding goal. Therefore, in this paper, we intend to answer the following question "what set of features determine a project's success?". We begin by studying the dynamics of Kickstarter, a popular reward-based crowdfunding platform, and the impact of social networks on this platform. Contrary to previous studies, our analysis is not restricted to project-based features alone; instead, we expand the features into four different categories: temporal traits, personal traits, geo-location traits, and network traits. Using a comprehensive dataset of 18K projects and 116K tweets, we provide several unique insights about these features and their effects on the success of Kickstarter projects. Based on these insights, we build a supervised learning framework to learn a model that can recommend a set of investors to Kickstarter projects. By utilizing features from the first three days of project duration alone, we show that our results are significantly better than the previous studies.
CREDBANK: A Large-Scale Social Media Corpus With Associated Credibility Annotations
Authors: Mitra Tanushree , Gilbert Eric
Published: November 2022

Social media has quickly risen to prominence as a news source, yet lingering doubts remain about its ability to spread rumor and misinformation. Systematically studying this phenomenon, however, has been difficult due to the need to collect large-scale, unbiased data along with in-situ judgements of its accuracy. In this paper we present CREDBANK, a corpus designed to bridge this gap by systematically combining machine and human computation. Specifically, CREDBANK is a corpus of tweets, topics, events and associated human credibility judgements. It is based on the real-time tracking of more than 1 billion streaming tweets over a period of more than three months, computational summarizations of those tweets, and intelligent routings of the tweet streams to human annotators — within a few hours of those events unfolding on Twitter. In total CREDBANK comprises more than 60 million tweets grouped into 1049 real-world events, each annotated by 30 human annotators. As an example, with CREDBANK one can quickly calculate that roughly 24% of the events in the global tweet stream are not perceived as credible. We have made CREDBANK publicly available, and hope it will enable new research questions related to online information credibility in fields such as social science, data mining and health.
Comparing Events Coverage in Online News and Social Media: The Case of Climate Change
Authors: Olteanu Alexandra , Castillo Carlos , Diakopoulos Nicholas , Aberer Karl
Published: November 2022

Social media is becoming more and more integrated in the distribution and consumption of news. How is news in social media different from mainstream news? This paper presents a comparative analysis covering a span of 17 months and hundreds of news events, using a method that combines automatic and manual annotations. We focus on climate change, a topic that is frequently present in the news through a number of arguments, from current practices and causes (e.g. fracking, CO2 emissions) to consequences and solutions (e.g. extreme weather, electric cars). The coverage that these different aspects receive is often dependent on how they are framed---typically by mainstream media. Yet, evidence suggests an existing gap between what the news media publishes online and what the general public shares in social media. Through the analysis of a series of events, including awareness campaigns, natural disasters, governmental meetings and publications, among others, we uncover differences in terms of the triggers, actions, and news values that are prevalent in both types of media. This methodology can be extended to other important topics present in the news.
Misalignment Between Supply and Demand of Quality Content in Peer Production Communities
Authors: Warncke-Wang Morten , Ranjan Vivek , Terveen Loren , Hecht Brent
Published: November 2022

In peer production communities, individual community members typically decide for themselves where to make contributions, often driven by factors such as “fun” or a belief that “information should be free”. However, the extent to which this bottom-up, interest-driven content production paradigm meets the needs of consumers of this content is unclear. In this paper, we introduce an analytical framework for studying the relationship between content production and consumption in peer production communities. Applying our framework to four large Wikipedia language editions, we find extensive misalignment between production and consumption in all of them. We also show that this misalignment has an enormous effect on Wikipedias readers. For example, over 1.5 billion monthly pageviews in the English Wikipedia go to articles that would be of much higher quality if editors optimally distributed their work to meet reader demand. Examining misalignment in more detail, we observe that there is an excess of high-quality content about certain specific topics, and that the majority of articles with insufficient quality are in a stable state (i.e. not breaking news). Finally, we discuss technolo- gies and community practises that can help reduce the misalignment between the supply of and demand for high-quality content in peer production communities.
Popularity Dynamics and Intrinsic Quality in Reddit and Hacker News
Authors: Stoddard Greg
Published: November 2022

In this paper we seek to understand the relationship between the online popularity of an article and its intrinsic quality. Prior experimental work suggests that the relationship between quality and popularity can be very distorted due to factors like social influence bias and inequality in visibility. We conduct a study of popularity on two different social news aggregators, Reddit and Hacker News. We define quality as the number of votes an article would have received if each article was shown, in a bias-free way, to an equal number of users. We propose a simple Poisson regression method to estimate this quality metric from time-series voting data. We validate our methods on data from Reddit and Hacker News, as well the experimental data from prior work. Using these estimates, we find that popularity on Reddit and Hacker News is a relatively strong reflection of intrinsic quality.
An Ethnomethodologically-Informed Approach to Interface Design for Social Interactions around Video Online
Authors: Zawilska Anna , Albury Steven
Published: November 2022

With the emergence of video on community media sites such as YouTube or TED, there is a need to understand interactions by community participants around video in order to maximise the potential of these systems to support communities and create meaningful interactions online. In this paper, we illustrate how ethnomethodologically-informed studies of the social interactions among a small group of co-located participants around video may be used to inform the design of an online video interface which may support interactions by a large distributed group of participants. Our point of view is grounded in the idea that the interactional accomplishments of a co-located group remain relevant at a distributed level, thereby allowing an ethnomethodologically-informed approach to arrive at effective implications for design of online systems. A strength of this approach is the potential to create implications which are properly grounded in a set of observations of the precise ways in which people interact to accomplish social interaction around video. For the purpose of illustration, we perform an analysis of a fragment of video data collected from a quasi-naturalistic experiment of a co-located group collaboratively annotating a video, from which we propose a number of design implications for a video annotation interface.
Audience and the Use of Minority Languages on Twitter
Authors: Nguyen Dong , Trieschnigg Dolf , Cornips Leonie
Published: November 2022

On Twitter, many users tweet in more than one language. In this study, we examine the use of two Dutch minority languages. Users can engage with different audiences and by analyzing different types of tweets, we find that characteristics of the audience influence whether a minority language is used. Furthermore, while most tweets are written in Dutch, in conversations users often switch to the minority language.
Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns
Authors: Li Huayi , Chen Zhiyuan , Mukherjee Arjun , Liu Bing , Shao Jidong
Published: November 2022

Although opinion spam (or fake review) detection has attracted significant research attention in recent years, the problem is far from solved. One key reason is that there is no large-scale ground truth labeled dataset available for model building. Some review hosting sites such as Yelp.com and Dianping.com have built fake review filtering systems to ensure the quality of their reviews, but their algorithms are trade secrets. Working with Dianping, we present the first large-scale analysis of restaurant reviews filtered by Dianping's fake review filtering system. Along with the analysis, we also propose some novel temporal and spatial features for supervised opinion spam detection. Our results show that these features significantly outperform existing state-of-art features.
A Comparative Study of Demographic Attribute Inference in Twitter
Authors: Chen Xin , Wang Yu , Agichtein Eugene , Wang Fusheng
Published: November 2022

Social media platforms have become a major gateway to receive and analyze public opinions. Understandingusers can provide invaluable context information of their social media posts and significantly improve traditional opinion analysis models. Demographic attributes,such as ethnicity, gender, age, among others,have been extensively applied to characterize social mediausers. While studies have shown that user groups formed by demographic attributes can have coherent opinions towards political issues, these attributes are often not explicitly coded by users through their profiles.Previous work has demonstrated the effectiveness of different user signals such as users’ posts and names in determining demographic attributes. Yet, these efforts mostly evaluate linguistic signals from users’ postsand train models from artificially balanced datasets. In this paper, we propose a comprehensive list of user signals:self-descriptions and posts aggregated from users’ friends and followers, users’ profile images, and users’ names.We provide a comparative study of these signalsside-by-side in the tasks on inferring three major demographic attributes, namely ethnicity, gender, and age.We utilize a realistic unbalanced datasets that share similar demographic makeups in Twitter for training modelsand evaluation experiments. Our experiments indicate that self-descriptions provide the strongest signal for ethnicity and age inference and clearly improve the overall performance when combined with tweets. Profile images for gender inference have the highest precision score with overall score close to the best result in our setting. This suggests that signals in self descriptions and profile images have potentials to facilitate demographic attribute inferences in Twitter, and are promising for future investigation.
It Is Not Only About Grievances: Emotional Dynamics in Social Media During the Brazilian Protests
Authors: Costa Jean , Rotabi Rahmtin , Murnane Elizabeth , Choudhury Tanzeem
Published: November 2022

In the summer of 2013, Brazil experienced a period of conflict triggered by a series of protests. While the popular press covered the events, little empirical work has investigated how first-hand reporting of the protests occurred and evolved over social media and how such exposure in turn impacted the demonstrations themselves. In this study we examine over 42 million tweets shared during the three months of conflict in order to uncover patterns in online and offline protest-related activity as well as to explore relationships between language-use in tweets and the emotions and underlying motivations of protesters. Our findings show that peaks in Twitter activity coincide with days in which heavy protesting took place, that the words in tweets reflect emotional characteristics of protest-related events, and less expectedly, that these emotions convey both positive as well as negative sentiment.
Tweet Enrichment for Effective Dimensions Classification in Online Reputation Management
Authors: McDonald Graham , Deveaud Romain , McCreadie Richard , Macdonald Craig , Ounis Iadh
Published: November 2022

Online Reputation Management (ORM) is concerned with the monitoring of public opinions on social media for entities such as commercial organisations. In particular, we investigate the task of reputation dimension classification, which aims to classify tweets that mention a business entity into different dimensions (e.g. "financial performance'' or "products and services''). However, producing a general reputation dimension classification system that can be used across businesses of different types is challenging, due to the brief nature of tweets and the lack of terms in tweets that relate to specific reputation dimensions. To tackle these issues, we propose a robust and effective tweet enrichment approach that expands tweets with additional discriminative terms from a contemporary Web corpus. Using the RepLab 2014 test collection, we show that our tweet enrichment approach outperforms effective baselines including the top performing submission to RepLab 2014. Moreover, we show that the achieved accuracy scores are very close to the upper bound that our approach could achieve on this collection.
The Importance of Play in Digital Placemaking
Authors: Sun Emily
Published: November 2022

In this position paper, I argue for the importance of play when designing systems for digital placemaking. I offer examples of existing placemaking systems that embody the openness prescribed by ludic design and provide an idea that could be further developed during the workshop: digital graffiti.
Everyone's Invited: A New Paradigm for Evaluation on Non-Transferable Datasets
Authors: Jurgens David , Finethy Tyler , Armstrong Caitrin , Ruths Derek
Published: November 2022

Social media data mining and analytics has stimulated a wide array of computational research. Traditionally, individual researchers are responsible for acquiring and managing their own datasets. However, the temporal nature of social data, the challenges involved in correctly preparing a dataset, the sheer scale of many datasets, and the proprietary nature of many data sources can make extending and comparing computational methods difficult and often impossible. In light of this, because replicability is a fundamental pillar of the scientific process and because method comparison is essential to characterizing computational advancements, we require an alternative to the traditional model of researcher-owned datasets. In this paper we propose FREESR, a framework that gives researchers the ability to develop and test method performance without requiring direct access to “shared” datasets. As a case study and first community resource, we have implemented the FREESR paradigm around the task of Tweet geolocation. The implementation showcases the clear suitability of this framework for the social media research context. Beyond the implementation, we see the FREESR paradigm as being an important step towards making study reproducibility and method comparison more principled and ubiquitous in the social media research community.
Determining the Influence of Reddit Posts on Wikipedia Pageviews
Authors: Moyer Daniel , Carson Samuel , Dye Thayne , Carson Richard , Goldbaum David
Published: November 2022

The activity of passive content consumers on social media sites is typically difficult to measure. This paper explores the activity of a subset of such consumers by looking at the influence on Wikipedia pageviews of one large Reddit community which frequently links to Wikipedia articles. The subreddit used in this analysis, /r/todayilearned (TIL), features a large number of posts on an eclectic set of topics, but excludes current events, which helps rule out the primary threat to being able to make causal statements. Wikipedia's public hourly pageview data provides a unique opportunity to study the influence of a Reddit post on a Wikipedia page at different time horizons. We here present analyses using posts from 2012 in TIL, showing that the week in which a post references a specific Wikipedia article is associated with a substantial increase in pageviews relative to prior and successive weeks. We then apply functional PCA to the dataset in order to characterize pageview dynamics. We also provide a qualitative analysis of the subset of Wikipedia topics posted to Reddit.
Transition to eBuses with Minimal Timetable Disruptions
Authors: Arbelaez Alejandro , Climent Laura
Published: November 2022

The implementation of a sustainable and efficient transportation network requires addressing multiple concerns such as driving anxiety and battery charging/discharging times. Currently, the average driving distance of electric buses (eBuses) ranges between 70KM and 200KM on a full charge, about 60% less than regular diesel buses. Therefore, the bus charging infrastructure must be implemented in a way that charging time and limited driving range will not impact the quality of the service. In this paper, we formulate this problem as a MIP model to identify the best location of fast charging stations with minimal disruptions in the current timetables of three cities in Ireland, i.e., Cork, Galway, and Limerick.
Solving Classical AI Planning Problems Using Planning-Independent CP Modeling and Search
Authors: Babaki Behrouz , Pesant Gilles , Quimper Claude-Guy
Published: November 2022

The combinatorial problems that constraint programming typically solves belong to the class of NP-hard problems. The AI planning community focuses on even harder problems: for example, classical planning is PSPACE-hard. A natural and well-known constraint programming approach to classical planning solves a succession of fixed plan-length problems, but with limited success. We revisit this approach in light of recent progress on general-purpose branching heuristics. We conduct an empirical comparison of our proposal against state-of-the-art planners.
From Multi-Agent Pathfinding to 3D Pipe Routing
Authors: Belov Gleb , Du Wenbo , Garcia De La Banda Maria , Harabor Daniel , Koenig Sven , Wei Xinrui
Published: November 2022

The 2D Multi-Agent Path Finding (MAPF) problem aims at finding collision-free paths for a number of agents, from a set of start locations to a set of goal positions in a known 2D environment. MAPF has been studied in theoretical computer science, robotics, and artificial intelligence over several decades, due to its importance for robot navigation. It is currently experiencing significant scientific progress due to its relevance in automated warehousing (such as those operated by Amazon) and in other contemporary application areas. In this paper, we demonstrate that some recently developed MAPF algorithms apply more broadly than currently believed in the MAPF research community. In particular, we describe the 3D Pipe Routing (PR) problem, which aims at placing collision-free pipes from given start locations to given goal locations in a known 3D environment. The MAPF and PR problems are similar: a solution to a MAPF instance is a set of blocked cells in x-y-t space, while a solution to the corresponding PR instance is a set of blocked cells in x-y-z space. We show how to use this similarity to apply several recently developed MAPF algorithms to the PR problem, and discuss their performance on real-world PR instances. This opens up a new direction of industrial relevance for the MAPF research community.
Applying Monte-Carlo Tree Search in HTN Planning
Authors: Wichlacz Julia , Höller Daniel , Torralba Alvaro , Hoffmann Jörg
Published: November 2022

Search methods are useful in hierarchical task network (HTN) planning to make performance less dependent on the domain knowledge provided, and to minimize plan costs. Here we investigate Monte-Carlo tree search (MCTS) as a new algorithmic alternative in HTN planning. We implement combinations of MCTS with heuristic search in PANDA. We furthermore investigate MCTS in JSHOP, to address lifted (non-grounded) planning, leveraging the fact that, in contrast to other search methods, MCTS does not require a grounded task representation. Our new methods yield coverage performance on par with the state of the art, but in addition can effectively minimize plan cost over time.
Exploiting Learned Policies in Focal Search
Authors: Araneda Pablo , Greco Matias , Baier Jorge A.
Published: November 2022

Recent machine-learning approaches to deterministic search and domain-independent planning employ policy learning to speed up search. Unfortunately, when attempting to solve a search problem by successively applying a policy, no guarantees can be given on solution quality. The problem of how to effectively use a learned policy within a bounded-suboptimal search algorithm remains largely as an open question. In this paper, we propose various ways in which such policies can be integrated into Focal Search, assuming that the policy is a neural network classifier. Furthermore, we provide mathematical foundations for some of the resulting algorithms. To evaluate the resulting algorithms over a number of policies with varying accuracy, we use synthetic policies which can be generated for a target accuracy for problems where the search space can be held in memory. We evaluate our focal search variants over three benchmark domains using our synthetic approach, and on the 15-puzzle using a neural network learned using 1.5 million examples. We observe that Discrepancy Focal Search, which we show expands the node which maximizes an approximation of the probability that its corresponding path is a prefix of an optimal path, obtains, in general, the best results in terms of runtime and solution quality.
Learning-Based Synthesis of Social Laws in STRIPS
Authors: Nir Ronen , Shleyfman Alexander , Karpas Erez
Published: November 2022

In a multi-agent environment, each agent must take into account not only the actions it must perform to achieve its goals, but also the behavior of other agents in the system, which usually requires some sort of coordination between the agents. One way to avoid the complexity of centralized planning and online negotiation between agents is to design an artificial social system. This system enacts a social law that restricts the behavior of the agents. A robust social law enables the agents to reach their goals while keeping them from interfering with each other. However, the problem of efficient synthesis of such laws is computationally hard, and previously proposed search techniques do not scale well. In this paper, we propose the use of graph neural networks to predict social laws from a graph-based representation of multi-agent systems. However, as this prediction can be wrong, we use heuristic search to correct possible mistakes in the network's prediction ensuring that the produced social law is indeed robust. Our empirical evaluation shows that this approach beat the previous state-of-the-art in social law synthesis, and that is can learn from an imperfect expert, even in the presence of noise.
Conflict-Free Multi-Agent Meeting
Authors: Atzmon Dor , Freiman Shahar Idan , Epshtein Oscar , Shichman Oran , Felner Ariel
Published: November 2022

Multi-Agent Meeting (MAM) is the problem of finding a meeting location for multiple agents and paths to that location. Recently, a Multi-Directional Heuristic Search algorithm, called MM*, was introduced. MM* is a state-of-the-art MAM optimal solver that searches from multiple directions (one for each agent) and is guided by a heuristic function. Practically, a solution to MAM may contain conflicting paths. A related problem that plans conflict-free paths to a given set of goal locations is the Multi-Agent Path Finding problem (MAPF). In this paper, we solve the Conflict-Free Multi-Agent Meeting problem (CF-MAM). In CF-MAM, we find a meeting location for multiple agents (as in MAM) as well as conflict-free paths (as in MAPF) to that location. We introduce two novel algorithms, which combine MAM and MAPF solvers, for optimally solving CF-MAM. We compare both algorithms experimentally, showing the pros and cons of each algorithm.
DPLL(MAPF): an Integration of Multi-Agent Path Finding and SAT Solving Technologies
Authors: Čapek Martin , Surynek Pavel
Published: November 2022

The task in multi-agent path finding (MAPF) is to find non-conflicting paths connecting agents' start and goal positions. The MAPF problem is often compiled to Boolean satisfiability (SAT) and solved by existing SAT solvers. Contemporary compilation approaches of MAPF to SAT regard the SAT solver as an external tool whose task is to return an assignment of all decision variables of a Boolean model of the input MAPF instance. We present in this paper a novel compilation scheme called DPLL(MAPF) in which the consistency checking of partial assignments of decision variables with respect to the MAPF rules is integrated directly into the SAT solver. This scheme allows for far more automated compilation where the SAT solver and the consistency checking procedure work together simultaneously to create the Boolean model and to search for its satisfying assignment.
Metareasoning for Interleaved Planning and Execution
Authors: Elboher Amihay , Shperberg Shahaf S. , Shimony Solomon E.
Published: November 2022

Agents that plan and act in the real world must deal with the fact that time passes as they are planning. In the presence of tight deadlines, there may be insufficient time to complete the search for a plan before it is time to act.
One can gain additional time to search by starting to act before a complete plan is found, incurring the risk of making incorrect action choices.
This tradeoff between opportunity and risk,
inherent in interleaving planning and
execution, is a non-trivial metareasoning problem addressed in this paper.
Multi-Goal Multi-Agent Path Finding via Decoupled and Integrated Goal Vertex Ordering
Authors: Surynek Pavel
Published: November 2022

We introduce multi-goal multi agent path finding (MG-MAPF) which generalizes the standard discrete multi-agent path finding (MAPF) problem. While the task in MAPF is to navigate agents in an undirected graph from their starting vertices to one individual goal vertex per agent, MG-MAPF assigns each agent multiple goal vertices and the task is to visit each of them at least once. Solving MG-MAPF not only requires finding collision free paths for individual agents but also determining the order of visiting agent's goal vertices so that common objectives like the sum-of-costs are optimized.
Multi-Agent Pickup and Delivery with Task Deadlines
Authors: Wu Xiaohu , Liu Yihao , Tang Xueyan , Cai Wentong , Bai Funing , Khonstantine Gilbert , Zhao Guopeng
Published: November 2022

We study the multi-agent pickup and delivery problem with task deadlines, where a team of agents execute tasks with individual deadlines to maximize the number of tasks completed by their deadlines. We take an integrated approach that assigns and plans one task at a time taking into account the agent states resulting from all the previous task assignments and path planning. We define metrics to effectively determine which agent ought to execute a given task and which task is most worth assignment next. We leverage the bounding technique to greatly improve the computational efficiency.
Weight Constrained Path Finding with Bidirectional A*
Authors: Ahmadi Saman , Tack Guido , Harabor Daniel , Kilby Philip
Published: November 2022

Weight constrained path finding, known as a challenging variant of the classic shortest path problem, aims to plan cost optimum paths whose weight/resource usage is limited by a side constraint. Given the bi-criteria nature of the problem (i.e., the presence of cost and weight), solutions to the Weight Constrained Shortest Path Problem (WCSPP) have some properties in common with bi-objective search. This paper leverages the state-of-the-art bi-objective search algorithm BOBA* and presents WC-BA*, an exact A*-based WCSPP method that explores the search space in different objective orderings bidirectionally. We also enrich WC-BA* with two novel heuristic tuning approaches that can significantly reduce the number of node expansions in the exhaustive search of A*. The results of our experiments on a large set of realistic problem instances show that our new algorithm solves all instances and outperforms the state-of-the-art WCSPP algorithms in various scenarios.
Iterative-Deepening Uniform-Cost Heuristic Search
Authors: Bu Zhaoxing , Korf Richard E.
Published: November 2022

Breadth-first heuristic search (BFHS) is a classic algorithm for optimally solving heuristic search and planning problems. BFHS is slower than A* but requires less memory. However, BFHS only works on unit-cost domains. We propose a new algorithm that extends BFHS to domains with different edge costs, which we call uniform-cost heuristic search (UCHS). Experimental results show that the iterative-deepening version of UCHS, IDUCHS, is slower than A* but requires less memory on a variety of planning domains.