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A perception that LGBTQ+ terminology is undergoing constant and rapid shift is often mobilized ideologically to justify resistance to transgender and/or non-binary people's language activism; however, there is little empirical work that explores this purported language change. How much is language use in queer and trans communities really changing, and how do changes occur? Additionally, there is criticism from within queer and trans communities that mainstream trans and queer spaces uphold and reinforce other hegemonic norms relating to whiteness, ableism, and classism. When language use in online queer and trans communities shifts, how might those with most power in the community influence the way that change unfolds? In this thesis, I explore these dynamics by examining shifting language patterns in an online trans LiveJournal community (TransLiveCorpus – Zimman & Hayworth 2020). Using bigram snapshot language models, I analyze community homogeneity over time, as well as identifying how top users pattern with respect to the overall community. This computational analysis is paired with qualitative analysis of how interactions of creating and enforcing language norms play out. Based on this, I argue that when approaching language change, we must consider how community language use exists within broader socio-political landscapes.

Online user innovation communities are becoming a promising source of user innovation knowledge and creative users. With the purpose of identifying valuable innovation knowledge and users, this study constructs an integrated super-network model, i.e., User Innovation Knowledge Super-Network (UIKSN), to integrate fragmented knowledge, knowledge fields, users and posts in an online community knowledge system. Based on the UIKSN, the core innovation knowledge, core innovation knowledge fields, core creative users, and the knowledge structure of individual users were identified specifically. The findings help capture the innovation trends of products, popular innovations and creative users, and makes contributions on mining, and integrating and analyzing innovation knowledge in community based innovation theory.

In precision marketing for online communities, the existing text-based methods of user positioning cannot position new users rapidly, and they have low positioning efficiency when there is a large number of users. This research proposes a systematic method for the positioning of online community users. In this method, text mining and clustering algorithms are combined to cluster users, and then the user clusters are effectively matched with users' basic attributes through a multinomial logistic regression model. By this means, efficient positioning under the circumstances of a rapid increase in new users and a large number of users can be achieved. Calculation results from a real world example show that this method can effectively solve the problems found in traditional user positioning methods and provides a productive new approach to community user positioning. The study also offers suggestions for user classification management from the perspective of precision marketing.

We develop smart contracts on public-permissionless blockchain to protect the music industry from the consequences of illegal downloading of copyright-protected music files. We develop a decentralized music file-sharing platform, where music file owners can upload the music files, and music file requesters can download the music files. We also establish a fair rewarding mechanism for the music industry for the benefit of music file owners, where music files can be traded through smart contracts between the music file owners and file requesters. Further, we implement a penalty scheme to avoid malicious and illegal music file resources to be added to the community network. This approach is ideal for addressing the problem of illegal distribution of copyright-protected music files without the consent of the owners, which has negative consequences in the music industry. This model considers a community of people networked together, where music file owners can upload their music files to a community network server on approval of the majority of the community and the file requesters can request a paid download from the community network. Music file uploading and downloading transactions are voted and signed by the online community members. All parties are rewarded with an incentive fee per valid transaction. Transactions are handled through self-executing smart contracts. This model further analyzes the costs incurred in music file uploading and downloading transactions based on the uploading/downloading cost, the transaction fee and the networking cost. Perceiving the significance of the smart contract in the music industry, we determine that the effect of illegal downloading of copyright-protected music files can be reduced. We also emphasize the challenges associated with the adoption of the smart contract. Since blockchain interoperability is yet an issue, migrating applications in a heterogeneous network or between platforms can be costly and tedious, which should be further explored in the future. Adapting the proposed architecture for private-permissioned blockchain (Hyperledger Fabric and Libra by Facebook, R3 Corda) and could be a natural extension of this work.

Aim: The main motto of the study is to optimize the large volume of data using data placement algorithm and online community adjustment algorithm and comparing their accuracy. Materials and Methods: Data placement algorithm (N=10) and online community adjustment (N=10) was iterated 20 times to optimize the data. Result and Discussion: Data placement algorithm has significantly better accuracy (85%) compared to online community adjustment algorithm(78%). The statistical significance of data placement (p

PurposeThis study, an exploratory research, aims to investigate social media users' expectations of information systems (IS) products that are conceived but not yet launched. It specifically analyses social media data from Twitter about forthcoming smartphones and smartwatches from Apple and Samsung, two firms known for their innovative gadgets.Design/methodology/approachTweets related to the following four forthcoming IS products were retrieved from 1st January 2020 to 30th September 2020: (1) Apple iPhone 12 (6,125 tweets), (2) Apple Watch 6 (553 tweets), (3) Samsung Galaxy Z Flip 2 (923 tweets) and (4) Samsung Galaxy Watch Active 3 (207 tweets). These 7,808 tweets were analysed using a combination of the Natural Language Processing Toolkit (NLTK) and sentiment analysis (SentiWordNet).FindingsThe online community was quite vocal about topics such as design, camera and hardware specifications. For all the forthcoming gadgets, the proportion of positive tweets exceeded that of negative tweets. The most prevalent sentiment expressed in Apple-related tweets was neutral, but in Samsung-related tweets was positive. Additionally, it was found that the proportion of tweets echoing negative sentiment was lower for Apple compared with Samsung.Originality/valueThis paper is the earliest empirical work to examine the degree to which social media chatter can be used by project managers for IS development projects, specifically for the purpose of end-users' expectation management.

Community detection that discovers all densely connected communities in a network has been studied a lot. In this paper, we study online community search for query-dependent communities, which is a different but practically useful task. Given a query vertex in a graph, the problem is to find meaningful communities that the vertex belongs to in an online manner. We propose a community model based on the k-truss concept, which brings nice structural and computational properties. We design a compact and elegant index structure which supports the efficient search of k-truss communities with a linear cost with respect to the community size. We also investigate the k-truss community search problem in a dynamic graph setting with frequent insertions and deletions of graph vertices and edges. In addition, to support k-truss community search over massive graphs which cannot entirely fit in main memory, we propose I/O-efficient algorithms for query processing under the semi-external model. Extensive experiments on massive real-world networks demonstrate the effectiveness of our k-truss community model, the efficiency, and the scalability of our in-memory and semi-external community search algorithms.

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.

Online community moderators often rely on social signals such as whether or not a user has an account or a profile page as clues that users may cause problems. Reliance on these clues can lead to overprofiling bias when moderators focus on these signals but overlook the misbehavior of others. We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible. We analyze moderator behavior in Wikipedia as mediated by RCFilters, a system which displays social signals and algorithmic flags, and estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially those by established editors with positive social signals, and that flagging decreases the likelihood that moderation actions will be undone. Our results suggest that algorithmic flagging systems can lead to increased fairness in some contexts but that the relationship is complex and contingent., Comment: 27 pages, 11 figures, ACM CSCW

Digital trace data enable researchers to study communication processes at a scale previously impossible. We combine social network analysis and automated content analysis to examine source and message factors’ impact on ratings of user-shared content. We found that the expertise of the author, the network position that the author occupies, and characteristics of the content the author creates have a significant impact on how others respond to that content. By observationally examining a large-scale online community, we provide a real-world test of how message consumers react to source and message characteristics. Our results show that it is important to think of online communication as occurring interactively between networks of individuals, and that the network positions people inhabit may inform their behavior.

Lead users are the group of most valuable users in product innovation and new product development for a certain firm. Based on web mining methods on user innovation communities, this paper presents quantitative methodology to evaluate the contributions and values of online community users to identify lead users. Firstly, we analyze user behaviors and calculate the user interest index (UII) of posts, keywords and innovation fields to measure the popularity of innovations based on other users' focus. Secondly, the model of User Innovation Knowledge supernetwork (UIKSN) is proposed, in which UIIs and user contributions are considered as two types of node weights for integrating behavior data and content data. And then, rules and methods are suggested based on the UIKSN model for identifying lead users to meet various requirements including contributions and UIIs in posts, keywords, hot frontiers, core innovation fields, and even in certain fields or knowledge points. Furthermore, knowledge structures are analyzed through ego-network analysis. Case studies show that the proposed UIKSN model and methodology are more objective and credible and thus have good potentials for identifying and analyzing lead users from multiple levels, such as posts, keywords, hot front, core innovation fields, etc.

This descriptive research aimed at providing a general description of primary school pre-service teachers’ competence level of computational concepts in programming with Scratch using Dr. Scratch. This study analyzed Scratch projects made by 87 sophomore students of the Primary School Teacher Education Program of Sanata Dharma University as part of their Media Pembelajaran Berbasis ICT (MPBICT) course. The projects were then submitted to the Scratch Online Community platform and analyzed using a web tool called Dr. Scratch to analyze their competence level of computational concepts. The analysis results provided by Dr. Scratch showed that 75.86% of the Scratch projects belong to the category of developing projects while 22.99% of them were categorized as master projects, and only 1.15% of the projects could be labeled as basic projects. The results also revealed that the most common bad coding practices identified in the submitted projects were duplicated scripts and object namings. These results indicated that the primary school pre-service teachers of the Primary School Teacher Education Program of Sanata Dharma University had moderate competence level in integrating Scratch computational concepts such as flow control, data representation, synchronization, and user interactivity but the pre-service teachers still needed to be provided with more opportunities to work with other Scratch computational concepts such as abstraction, parallelism, and logic.

Tagging is a defining characteristic of Web 2.0. It allows users of social computing systems (e.g., question and answering (Q&A) sites) to use free terms to annotate content. However, is tagging really a free action? Existing work has shown that users can develop implicit consensus about what tags best describe the content in an online community. However, there has been no work studying the regularities in how users order tags during tagging. In this paper, we focus on the natural ordering of tags in domain-specific Q&A sites. We study tag sequences of millions of questions in four Q&A sites, i.e., CodeProject, SegmentFault, Biostars, and CareerCup. Our results show that users of these Q&A sites can develop implicit consensus about in which order they should assign tags to questions. We study the relationships between tags that can explain the emergence of natural ordering of tags. Our study opens the path to improve existing tag recommendation and Q&A site navigation by leveraging the natural ordering of tags.

Constructing a high-active EVCARD online community (EOC)(HEOC) is a particularly effective approach for the emerging car-sharing company to trumpet its new products/services and stabilize its customer base. Although there is plenty of research on the model of user-generated content (UGC), the model of interaction between users and marketers on the generation and adoption of UGC and marketer- generated content (MGC) in the online marketing community is very much under-explored. To fill this gap, this study proposes the user content adoption and generation fuzzy system dynamic model (UCAAGFSDM) for building a HEOC where offline activities and online experience topics are mixed in a dynamic way, namely Offline-to-Online (O2O) content continuous interaction (OCCI). In UCAAGFSDM, user content adoption model for MGC is constructed based on the information adoption theory and user content generation model is developed according to the social cognition theory. And subsequently, the system dynamic model (SDM) is built for completely and meticulously describing mechanisms of user content adoption and generation and the interaction between users and marketers on the content in EOC. Since the cognitive behaviors of user in the process of adoption and generation content are uncertain and vague, to further improve the accuracy of the proposed model, Zadeh’s extension principle and α-cut concept are used to solve the uncertain factors, which can flexibly and accurately descript the fuzzy cognitive behaviors of user variables. Based on UCAAGFSDM, the complex mechanism of OCCI in EOC is accurately depicted and optimal construction strategies for EOC are provided for community managers by comprehensively analyzing the cost, income and profit of EOC.

This paper describes Project Spectra, a collective of open source developers that aims to build free and open source voice training technology for transgender people. We demonstrate how a design prioritizing the agency of trans users was made possible through sustained community collaboration. Using an autoethnographic approach, we discuss our community-based design process, which was documented with memos, online meetings and text conversations, sketches, and other data sources. We illustrate how we articulated our values as a group: deciding our programming framework (including a Statement of Principles), elaborating our "Experience Goals" (the feelings we wanted our design to elicit), and determining the features we wanted to implement in our app. We conclude with a reflection on the benefits and challenges of conducting community-based design research through an open-source organizational model.

Almost all of the existing approaches to determining online local community are typically deliberated like-minded users who have similar topical interests. However, such methodologies overlook the prospective temporality of users’ interests as well as users’ degree of topical activeness. As a result, the consequential communities might have extremely lower active users. This research investigates how online social users’ behaviors and topical activeness vary over time and how these parameters can be employed in order to improve the quality of the detected local community. For a given input query, consisting a query node (user) and a set of attributes, this research intends to find densely-connected community in which community members are temporally similar in terms of their activities related to the query attributes. To address the proposed problem, we develop a temporal activity biased weight model which gives higher weight to users’ recent activities and develop an algorithm to search an effective community. The effectiveness of the proposed methodology is justified using four benchmark datasets and compared with four other baseline methods. Experimental results demonstrate that our proposed framework yields better outcomes than the baseline methods for all four benchmark datasets.

With the emergence and rapid proliferation of social media platforms and social networking sites, recent years have witnessed a surge of misinformation spreading in our daily life. Drawing on a large-scale dataset which covers more than 1.4M posts and 18M comments, we investigate the propagation of two distinct narratives--(i) conspiracy information, whose claims are generally unsubstantiated and thus referred as misinformation to some extent, and (ii) scientific information, whose origins are generally readily identifiable and verifiable--in an online social media platform. We find that conspiracy cascades tend to propagate in a multigenerational branching process while science cascades are more likely to grow in a breadth-first manner. Specifically, conspiracy information triggers larger cascades, involves more users and generations, persists longer, is more viral and bursty than science information. Content analysis reveals that conspiracy cascades contain more negative words and emotional words which convey anger, fear, disgust, surprise and trust. We also find that conspiracy cascades are more concerned with political and controversial topics. After applying machine learning models, we achieve an AUC score of nearly 90% in discriminating conspiracy from science narratives. We find that conspiracy cascades are more likely to be controlled by a broader set of users than science cascades, imposing new challenges on the management of misinformation. Although political affinity is thought to affect the consumption of misinformation, there is very little evidence that political orientation of the information source plays a role during the propagation of conspiracy information. Our study provides complementing evidence to current misinformation research and has practical policy implications to stem the propagation and mitigate the influence of misinformation online., Comment: 24 pages, 9 figures, 3 tables

Compared with stock market, cryptocurrency market is more susceptible to investor sentiment at the lack of substantial asset support. This study develops a proxy to measure the investor sentiment of cryptocurrency market by using textual analytics on millions of posts in Chain Node, which is the most active online community for Chinese cryptocurrency investors. We investigate the correlation between the sentiment and the market return from Jan. 2018 to Aug. 2020. The study argues that the proposed proxy could well reflect the investor sentiment of the cryptocurrency.

We address the problem of recommending online communities on social media platforms using design science. Our method is grounded in network science and leverages the random surfer model of the web, small-world networks, strength of weak connections, and connectivity to analyze three types of large-scale networks. In doing so, we design features for structural hole assortativity and local clustering coefficient rank to capture both the diversity and evolution of user interests. We also extract general online community features such as size and overlap. Experiments conducted on a large dataset of 34,000 lists created and subscribed to by 1,600 active Twitter users over a six-month period showed that our network features outperform the general and content features in terms of recommending communities at the top position. In addition, a combination of general and network features generated the best results in the top position with a significant performance improvement over using only the content features. A combination of all three types of features gave the best results in the top-5 and top-10 positions while improving the quality of recommendations at every other position. Our work outperforms the latest work on community recommendations on social media platforms and has major implications for the design of online community recommenders.

Members of an online community peer-produce digital artifacts by negotiating different perspectives and personal knowledge bases. These negotiations are manifested in the temporal evolution of the peer-produced artifact. In this study, we conceptualize the evolution of a digital artifact as a trajectory in a feature space. Our theoretical frame suggests that, through negotiations, contributors’ actions “pull” the trajectory and shape its movement in the feature space. We hypothesize that the type of contributors that work on a focal article influences the extent to which that article’s trajectory explores alternative positions within that space, and that the trajectory’s exploration is, in turn, associated with the artifact’s quality. To test these hypotheses, we analyzed the trajectories of wiki articles drawn from two peer-production communities, Wikipedia and Wikia, tracking the evolution of 242 paired articles for over a decade during which the articles went through 536,745 revisions. We found that the contributors who are the most likely to increase the trajectory’s exploration are those that (1) return to work on the focal artifact and (2) are unregistered members in the broader online community. Further, our results show that the trajectory’s exploration has a curvilinear association with article quality, indicating that exploration contributes positively to quality, but that the effect is reversed when exploration exceeds a certain level. The insights derived from this study highlight the value of an artifact-centric approach to increasing our understanding of the dynamics underlying peer-production.

A wide range of public and private organizations, businesses and individuals throughout the world are experimenting with web-based, GIS, virtual and mobile technologies, often in collaboration with each other in order to develop civic technologies. Online communities form the basis for civic platforms, which are incubators for new ideas through peer-to-peer networks, stakeholder mobilization and collaborative and partnership engagement. Digital data analytics offers unprecedented opportunities to build capacity within communities simply because online platforms serve as acquisition hubs of structured and unstructured data. Notwithstanding, technology powered forms of engagement face multiple design and management challenges. Currently, limited research is being conducted into the online community using digital data analytics in non-business contexts. This explorative study further develops existing knowledge on civic tech communities and the use of digital data analytics. To this end, we review the previous research in order to understand how to aggregate literature on online communities, civic technologies and data analytics into a conceptual model that is useable by both academics and practitioners. The framework proposed serves as the first step in differentiating the building blocks of relevant digital data in managing civic technology communities and should be regarded as an effort to structure the available sources. Thus, the focus of the proposed conceptual model is not to offer prescriptive guidelines on the use of digital analytics, but rather to present a broader understanding of the emerging phenomenon and dimensions of data analysis.

The social media revolution has provided the online community an opportunity and facility to communicate their views, opinions and intentions about events, policies, services and products. The intent identification aims at detecting intents from user reviews, i.e., whether a given user review contains intention or not. The intent identification, also called intent mining, assists business organizations in identifying user’s purchase intentions. The prior works have focused on using only the CNN model to perform the feature extraction without retaining the sequence correlation. Moreover, many recent studies have applied classical feature representation techniques followed by a machine learning classifier. We examine the intention review identification problem using a deep learning model with an emphasis on maintaining the sequence correlation and also to retain information for a long time span. The proposed method consists of the convolutional neural network along with long short-term memory for efficient detection of intention in a given review, i.e., whether the review is an intent vs non-intent. The experimental results depict that the performance of the proposed system is better with respect to the baseline techniques with an accuracy of 92% for Dataset1 and 94% for Dataset2. Moreover, statistical analysis also depicts the effectiveness of the proposed method with respect to the comparing methods.

Online communities are the most popular interactive environments on the Internet, which provide users with a platform to share their knowledge and expertise. The most important use of online communities in cyberspace is sharing knowledge. These communities are a great place to ask questions and find answers. The important challenges of these communities are the large volume of information and the lack of a method to determine their validity as well as expert finding which attracted a lot of attention in both industry and academia in. Therefore, identifying persons with relevant knowledge on a given topic and ranking them according to their expertise score can help to calculate the accuracy of the comments submitted on the internet. In this research, a model for finding experts and determining their domain expertise level by the aid of statistical calculations and the ant colony algorithm in the MetaFilter online community was presented. The WordNet Dictionary was used to determine the relevance of the user’s questions with the intended domain. The proposed algorithm determines the level of people’s expertise in the intended field by using the pheromone section of the Ant colony algorithm, which is based on the similarity of the questions sent by the users and the shared knowledge of the users from their interactions in the online community

The questions we can ask currently, building on decades of research, call for advanced methods and understanding. We now have large, complex data sets that require more than complex statistical analysis to yield human answers. Yet as some researchers have pointed out, we also have challenges, especially in computational social science. In a recent project I faced several such challenges and eventually realized that the relevant issues were familiar to users of free and open-source software. I needed a team with diverse skills and knowledge to tackle methods, theories, and topics. We needed to iterate over the entire project: from the initial theories to the data to the methods to the results. We had to understand how to work when some data was freely available but other data that might benefit the research was not. More broadly, computational social scientists may need creative solutions to slippery problems, such as restrictions imposed by terms of service for sites from which we wish to gather data. Are these terms legal, are they enforced, or do our institutional review boards care? Lastly—perhaps most importantly and dauntingly—we may need to challenge laws relating to digital data and access, although so far this conflict has been rare. Can we succeed as open-source advocates have?

Social integration through communication with family and friends can fulfill human’s desires of being cherished and respected. Such communications are very important for the elderly people, especially for those who have retired. Online social communities can help with this and provide positive effect on elderly people. But the elderly are quite reluctant to work with new technologies and hence, researchers have tried to implement specially designed social media application in easy user-interface devices for the elderly. In this paper, we conduct a Systematic Literature Review (SLR) method to collect and review studies to understand the different user-interaction devices used for the elderly to promote social connection. 33 literature papers were identified within the years 2013–2019 following a review procedure, which presents research on online social communities for elders. The papers are analyzed and classified further to understand the current state-of-the-art focus. This study further offers related discussion and conclusions.

Recently, with the advance of the wireless Internet access via mobile devices, a myriad of game development companies have forayed into the mobile game market, leading to intense competition. To survive in this fierce competition, mobile game developers often try to get a grasp of the rapidly changing needs of their customers by operating their own official communities where game users freely leave their requests, suggestions, and ideas relevant to focal games. Based on the heuristic-systematic model (HSM) and the data quality (DQ) framework, this study derives key content, non-content, and hybrid cues that can be utilized when game developers accept suggested postings in these online communities. The results of hierarchical multiple regression analysis show that relevancy, timeliness, amount of writing, and the number of comments are positively associated with mobile game developers’ knowledge adoption. In addition, title attractiveness mitigates the relationship between amount of writing/the number of comments and knowledge adoption.

This article explores how cycling tourists build trust in the process of transition from online community to offline travel companions. It uses data collected from participant observation and interviews of a cycling tourist group in China. The findings indicate that after building a social circle in an online community, the members of the “community” build trust by: identifying travel companions’ attitudes, values, knowledge, and experiences to build dispositional trust; identifying companions’ preferences, activities, or the patterns of cycling behavior to build institutional trust; identifying companions’ cycling experiences, occupations, and hobbies to build interpersonal trust. After the identification of travel companions and trust has developed, online community members make the decision to travel together as companions and their online social circle becomes a regulated group without hierarchy. A “triple-jump” explanatory model to explain the trust-building process and practical recommendations from these insights were outlined.

The diffusion of new product information in firm–hosted online communities depends on the information interaction between users. Especially in the early stage of new product online sales, user influence based on interactive behavior is the critical factor that affects the wide and sustainable diffusion of new product information, makes users respond to firm’s announcement of new product releases in time, and improves the new product competitiveness. Since very little work has been done on the effect of user influence on the new product information diffusion, this paper proposes a model based on user influence for the diffusion of new product information in firm–hosted online communities to fill this gap. Moreover, focusing on the hot topics of new product innovation functions, we present a new method to measure user influence from the perspective of user interaction, which provides a quantitative theoretical basis for the diffusion probability of influencing user state change in new product information diffusion. Finally, our model tests a well–known firm–hosted online community in China. The results reveal that the model can effectively describe the dynamic evolution of information diffusion, explain the key dynamic factors, and accurately predict the peak time of information diffusion.

Bringing together considerations from three research trends (honest signals of collaboration, socio-semantic networks and homophily theory), we hypothesise that word use similarity and having similar social network positions are linked with the level of employees’ digital interaction. To verify our hypothesis, we analyse the communication of close to 1600 employees, interacting on the intranet communication forum of a large company. We study their social dynamics and the ‘honest signals’ that, in past research, proved to be conducive to employees’ engagement and collaboration. We find that word use similarity is the main driver of interaction, much more than other language characteristics or similarity in network position. Our results suggest carefully choosing the language according to the target audience and have practical implications for both company managers and online community administrators. Understanding how to better use language could, for example, support the development of knowledge sharing practices or internal communication campaigns.

We Love Weather is the fan community of The Weather Channel. Launched in 2016, We Love Weather aims to serve so-called “weather geeks” by providing exclusive and specialized weather content, as well as participatory and communal elements. This study proposes that We Love Weather is an “affinity space” where participants create, procure, and develop content and knowledge. It exemplifies the power and capability of a high-functioning, efficient online information hub. Using digital ethnographic analysis of We Love Weather’s discussion forums and user-generated content, we found that We Love Weather promotes teaching and learning of weather information through sharing and community maintenance that establishes the site as an affinity space. It is an example of a positive, helpful, and deliberative online space.

A social crowdsourcing community (SCC) is a moderated online community where members participate in activities designed to elicit their opinions concerning topics related to products or services. This emerging paradigm of human computation systems is distinct in which it combines the powers of crowdsourcing and social networking to allow for systematic querying of crowds and synthesizing response data (i.e., contributions) into coherent reports for decision-makers. Since the SCC paradigm is relatively new, we apply methods from social network analysis and related fields to help answer important questions relevant to SCCs. We demonstrate the applicability of these methods by analyzing five years of interactions in a private SCC starting from its inception. This analysis reveals the patterns in members’ interactions within the SCC and key factors that affect them. In addition, the analysis reveals the patterns in the evolution of these interactions.

User-centered analysis is one of the aims of online community search. In this paper, we study personalized top-n influential community search that has a practical application. Given an evolving social network, where every edge has a propagation probability, we propose a maximal pk-Clique community model, that uses a new cohesive criterion. The criterion requires that the propagation probability of each edge or each maximal influence path between two vertices that is considered as an edge, is greater than p. The maximal clique problem is an NP-hard problem, and the introduction of this cohesive criterion makes things worse, as it may add new edges to existing networks. To conduct personalized top-n influential community search efficiently in such networks, we first introduce a search space refinement method. We then present pruning based and heuristic based search approaches. The proposed algorithms more than double the efficiency of the search performance for basic solutions. The effectiveness and efficiency of our algorithms have been verified using four real datasets.

Starting from the importance of innovative solutions for improving the needs of different practitioners as flood risk managers, the purpose of this review was to describe and analyze, evaluates, and prioritizes the various available different innovative solutions that have sufficient potential to be useful and used by practitioners. A systematic review of the literature was conducted using the DAREnet knowledge base (an integral feature of the DAREnet online community platform) which identified critical challenges for flood management and the relevant field or source of innovation, as well as the current scientific literature in the field of disaster studies. A fourth stage selection procedure identified candidate original or review papers and evaluated the degree to which papers met predetermined requirements for inclusion extracted from prior systematic reviews. Included in the study were over 100 studies that met the requirements for predetermined inclusion. The findings of this review showed that there is a huge untapped potential for innovative solutions in the field of prevention, preparedness, civil protection, communication, cooperation, etc. The findings of this review contribute to a growing body of knowledge regarding innovative solutions for flood risk management useful for practitioners.

Crowdsourcing ideas from users in online communities are becoming common practice in the development of high-tech products. While most studies focus on sourcing ideas from ordinary users (i.e., non-experts), high-tech firms commonly rely more on ‘lead users’. Despite the importance of lead users' peer-evaluation activities on a firm's user community platform, research on the relationship between these activities and a firm's actual implementation decision is scarce. We draw on lead user theory and a large dataset from a high-tech firm's platform to examine whether an idea's popularity, the comments it receives and sentiment are good predictors of a firm's consideration and implementation decisions. We find that in addition to the number of votes and comments, sentiment is a predictor of an idea's further development and implementation. Furthermore, implementing a lead user's ideas decreases the user's motivation marginally to contribute continuously to the platform. These findings have implications for the design and operations management of user community platforms.

One of the key objectives of data analysis in circadian research is to quantify the rhythmic properties of the experimental data. BioDare2 is a free, online service which provides fast timeseries analysis, attractive visualizations, and data sharing. This chapter outlines the description of an experiment for BioDare2 and how to upload and analyze the numerical timeseries data.

We live in a hyper-informed society that is constantly being fed with information stimuli. That information may not be correct, and society may be vulnerable to it. We present a methodological proposal with a mixed approach that allows the learning of the characteristics and weaknesses of news consumers in the face of disinformation. Said methodology moves away from the traditional model, and with it a new, much more complete and complex way of conducting discussion groups is carried out. The qualitative approach is carried out through the creation of an online community in which subjects are encouraged to participate in different activities and tests. On the other hand, in order to obtain quantitative data, a quasi-experimental survey where respondents are exposed to various stimuli created ad hoc, which seeks to measure the interest and credibility of different news items through an orthogonal design, is carried out. The use of this methodology will allow for an expansive and intensive approach to the knowledge of societal vulnerability factors, and with the subsequent results, a solid basis of disinformation can be established, which will allow for the development of a series of strategies to combat disinformation.

Decentralized ecosystems are generally still rarely used in various disciplines. Of all the existing fields, decentralization is only applying to solve problems requiring various partners' organizing. Blockchain has unique characteristics and reflects multiple values related to distinctive and prevalent influences is often referred to by the online community as crypto. This study describes Blockchain-mediated decentralization based on a systematic review on decentralization. This review covers the features of decentralization in a variety of disciplines starting with public administration to science. Blockchain as an intermediary referred to in various scientific articles as an opponent of the binary position interpreted, decentralized and centralized. The author proposes a dialectical design and argues that system jurisdiction allotment is a standard located on the scale amongst pure or centralized decentralization and then notes how a Blockchain arrangement can simultaneously have entities that are significantly centralized whereas others are not. Through a systematic review, we offer a design of Blockchain-mediated decentralization comprehension by having the authors document the findings based on their systematic review. Their proposed framework for generating Blockchain-mediated decentralization comprehension recommends a description and then drawing a new direction for future research on humans into distributed ledger technology.

SCRATCH allows students to imagine, program, and share their own creative ideas. One of the most interesting features of SCRATCH is the online community of users. SCRATCH is intended to be accessible to a wide audience with a range of abilities. SCRATCH also enables users to create complex projects and is used as part of many college-level computer science courses as an introduction to programming. When using SCRATCH, the best advice offered to teachers is to get out of the way and see what the students create. After our students have acquired the basic skills of programming with SCRATCH, we could further develop their programming skills and creativity by creating specific challenge problems for our students to solve.

The unprecedented COVID scenario has exponentially increased the exposure of internet to adolescent age group of the world population. The sudden extended online presence of this age group has created an area of concern in parenting fraternity as this is accompanied by restrictions on the normal social life of children. However, study states that direct parental control methods lessen the trust and the bond between the parent and the teen. If the teens are explained about the possible consequences of their online actions in a friendly manner, it helps them to understand the need of self-regulation and caution when they are being part of online community actively or passively. In this work, a self- regulation model is proposed. The self-regulation model is developed as a Mobile app called “Teen-alyse”. This app is designed to help in achieving a balance in parental guidelines and teen self-regulation. Further, the idea of active mediation has been included which encourages both, parent, and teen to have conversation for the time limit set for the mobile apps. By applying an analysis algorithm, the teen's YouTube activities are classified as flagged and not flagged contents and a report is generated, which alerts teens as well as parents. The work proposes the model of teen self-regulating app by following values of parental control, teen self-monitoring and active mediation.

The massive growth of social media for the past few decades has given a new era to the open web. People are using social media to openly share opinions and also discussed sensitive subjects such as mental health. This paper study the online community on the Reddit discussion forum. We focused on studying linguistic behavior in the online mental health community. We report linguistic analysis in order to understand the similarity and differences of each mental health community. We first study the sentiment per each Reddit community and followed by the topic modeling. By using the well-known Latent Dirichlet Allocation (LDA) method, we extract the most discussed topic for each community. By utilizing all related posts from each subreddits in each community, we establish the following exciting insights: (a) We observe that the headline of the post does not indicate the whole content of the discussion post, (b) we found that sentiment across the mental health community was high on positive compared to negative, and (c) topic distribution for each community varies but there exist similarities among them.

In this study, we applied data mining tools to build a business intelligence platform combining different information, obtained through social media mining, and integrating it with information about the characteristics of movies and their box offices. We used data from the social media following of both the film and its main actors. In addition, data was collected from the number of movie publications on Facebook, Instagram, and Twitter, as well as the corresponding engagement. We built different prediction models of box office revenue by empirically incorporating the effect of the movie’s community on social media (movie and actors’ followers), its activity on social media, and users’ reactions to the movie’s activity. Our analyses are based on Fama-Macbeth two-step regressions applied to panel data. The prediction models confirm the value of social media posts and their engagement, especially on Facebook and on Instagram, to increase the film’s performance. The results show that the volume of the movie’s and actors’ followers, the actions, and the users’ reactions on social media positively affect the box office. Then, the study supports the importance of building a business intelligence system, so companies can take advantage of their social media accounts. Film studios must be proactive with the access and the treatment of different data sources available to the movie’s online community through data mining techniques to attract more viewers and to increase their box office.

A huge amount of information is spread in the network, and each kind of information contains the possibility of influencing public opinion. However, considering the number of reasons, it is not realistic to review the information transmitted by users. It is of practical value to both the development of enterprises and the work of the government to analyze the network contents that are likely to become popular information in the future by means of prediction. This paper makes an in-depth study on the relevant theories of online community user influence and network content popularity, and summarizes the methods and shortcomings of domestic and foreign mainstream research. On this basis, the user influence measurement model based on PageRank and LDA and the post popularity measurement model based on neural network are constructed. In this paper, the Topic factor is innovatively brought into the measuring range of user influence, and the topic-relation Rank measurement model of user influence is established based on PageRank. Aiming at Baidu Tieba, the article uses crawlers to obtain real data and conduct experiments. The results show that the user influence measurement model integrates three influencing factors, namely user, network topology and text topic, so the model has a good measuring effect.

Online health communities provide a space where people seek out and provide support for weight loss activities, including tracking. Our study examined the experiences of members of an online community (r/loseit on Reddit.com) who posted about using digital tracking tools for weight loss. A targeted search garnered 379 public posts, which were analyzed using Thematic Analysis. Four themes reflected members’ individual and shared experiences: Tracking as gaining insight, Tracking as a vehicle of control, Confronting challenges in sustaining tracking and Teaching and learning the skills of tracking. We highlight complex socio-technical processes that members developed around tracking tools and discuss how knowledge of these appropriations can be applied to designing future user-centered tracking tools to support weight loss. We discuss how the social context of an online health community can shape both the usage of tracking tools and self-regulatory processes for health behaviour change.

The aim of this study is to propose an automatic and real-time social media analytics framework with interactive data visualizations to support effective exploration of knowledge about adverse drug reaction (ADR) surveillance. This proposed framework has been prototypically implemented on the basis of social media data. A longitudinal diabetes patient online community data (AskaPatient.com) as well as FDA Adverse Event Reporting Systems (FAERS) data as a benchmark were used to evaluate our proposed approach’s performance. Based on the results, our approach significantly increases the precision and accuracy for ADR extraction. The number of ADR cases, the time when the ADRs occurred, and the rating of Glucophage have been visualized that resulted by mining a collection of 870 ADRs posted in Askapatents.com over a certain time period (from 2001 to 2015). The results have important implications for pharmaceutical companies and hospitals wishing to monitor ADRs of medicines.

As social network structures evolve constantly, it is necessary to design an efficient mechanism to track the influential nodes and accurate communities in the networks. The attributed graph represents the information about properties of the nodes and relationships between different nodes, hence, this attribute information can be used for more accurate community detection. Current techniques of community detection do not consider the attribute or keyword information associated with the nodes in a graph. In this paper, we propose a novel algorithm of online community detection using a technique of keyword search over the attributed graph. First, the influential attributes are derived based on the probability of occurrence of each attribute type-value pair on all nodes and edges, respectively. Then, a compact Keyword Attribute Signature is created for each node based on the unique id of each influential attribute. The attributes on each node are classified into different classes, and this class information is assigned on each node to derive the strongest association among different nodes. Once the class information is assigned to all the nodes, we use a keyword search technique to derive a community of nodes belonging to the same class. The keyword search technique makes it possible to search community of nodes in an online and computationally efficient manner compared to the existing techniques. The experimental analysis shows that the proposed method derive the community of nodes in an online manner. The nodes in a community are strongly connected to each other and share common attributes. Thus, the community detection can be advanced by using keyword search method, which allows personalized and generalized communities to be retrieved in an online manner.

Theories of social exchange and social identity are extensively introduced to explain the motivation to share online. However, the first step to be social or identified in an online community is to have a profile name because online status and reputation should be assigned to an owner. The effect of profile name on online sharing is rarely explored in the literature, which is crucial in understanding the initial impulse of online sharing. We select a unique platform that enables users to use a profile name or remain anonymous and collect 2,109,555 reviews. Two main findings are drawn from our big data analysis. (1) Users who provide profile names are more involved in online sharing: they write longer text and upload more photos in comparison with anonymous users. (2) Users with profile names gain more recognition from peers. This study concludes by presenting the theoretical and managerial implications of these findings.

Complexity theory is the study of complex systems composed of multiple interacting agents that function as a whole. A complexity-informed theory of social self-organisation is introduced and applie...

The present study explores the phenomenon of remixing in product design for additive manufacturing (AM). In contrast to other manufacturing techniques, AM offers unprecedented flexibility in adapting existing product designs to changing requirements. However, in order to benefit from this potential, structured design procedures and tools are indispensable. As a possible solution, online platforms for collaborative 3D model creation are increasingly implementing features for remixing, a concept describing the creation of new models on the foundation of existing design elements. Against this backdrop, the objective of this research is to provide evidence for the value of remixing as an organizational intervention for improving product design processes. To this end, we present a mixed methods approach using data from Thingiverse, the world's largest AM-related online community. In a first step, we investigate qualitative data from 81 individual remix-based designs to identify the underlying mechanisms of remixing. We identify six such mechanisms that can further be grouped by the intended outcome of the respective process (creativity-oriented: inspiration, play, learning; productivity-oriented: speed, improvement, empowerment). In a second step, we turn to a quantitative analysis of platform data, which indicates that remixing may lead to better design process outcomes in terms of quantity and diversity of designs. Furthermore, we find that designs created by remixing designers are significantly more often printed by community members suggesting that remixing helps ensure manufacturing compatibility akin to continuous process improvement. Our research has several implications for individual designers and organizations engaging with product design for AM.

The development of information technology and social networks has created new opportunities to access lifelong learning in the form of informal learning. In an informal learning environment...