Telegram emerged as a crucial platform for both parties during the conflict between Russia and Ukraine. Per its minimal policies for content moderation, Pro-Kremlin narratives and potential misinformation were spread on Telegram, while anti-Kremlin narratives with related content were also propagated, such as war footage, troop movements, maps of bomb shelters, and air raid warnings. This paper presents a dataset of posts from both pro-Kremlin and anti-Kremlin Telegram channels, collected over a period spanning a year before and a year after the Russian invasion. The dataset comprises 404 pro-Kremlin channels with 4,109,645 posts and 114 anti-Kremlin channels with 1,117,768 posts. We provide details on the data collection process, processing methods, and dataset characterization. Lastly, we discuss the potential research opportunities this dataset may enable researchers across various disciplines.

Historically, online data has provided meaningful insights for information mining, leading to the adoption of knowledge graphs for application to online data. Knowledge embedding has become an important aspect of encoding and decoding links, relationships, and predicting the ties of an entity to an existing knowledge graph. This study applied topic modeling to extract topics, entities, and themes from heterogeneous web data from different sources around the Indo-Pacific region and modeled a knowledge graph. The knowledge graph was subjected to knowledge embedding by applying four scoring mechanisms: ComplEx, TransE, DistMult, and HolE, on a domain knowledge graph of Indo-Pacific Belt and Road initiatives to determine whether it was capable of revealing missing insights. This work significantly uses knowledge graphs and embedding to understand socioeconomic-related discussions online. Valuable insights were gained from the data in this research's clustering results of knowledge embedding. Important themes such as NASAKOM and BRI were identified in Cluster 0. Cluster 1 contained themes that discussed Marxist movements synonymous with Indonesia, and Cluster 2 showed themes on China's road policies, such as Asia-Pacific Economic Cooperation and Export-Import Bank China. Cluster 3 focused mainly on China's economic policies and the Philippines. Overall, this study demonstrates the usefulness of topic modeling and knowledge embedding in uncovering insights from online data and has implications for understanding socioeconomic trends in the Indo-Pacific region.

Recommender systems play a crucial role in suggesting relevant content to users based on their past activities. They employ a process known as "collaborative filtering" to efficiently navigate extensive content repositories. However, concerns have been raised regarding the potential bias and homogeneity in recommendations, resulting in filter-bubbles and echo-chambers. Detecting and mitigating these biases is crucial for ensuring fair and diverse automated decision-making systems. This study investigates the impact of YouTube's recommendation algorithm on three distinct narratives across multiple dimensions. Our objective is to identify potential biases and gain insights into its decision-making behavior. We applied a multi-method approach to evaluate emotional content, moral foundations, lexical similarity, and social network analysis across 5 depths of YouTube recommendations. The results of our analysis showed diversity in emotions, significant drift in topics, and a push toward non-related, but highly influential videos across multiple recommendation depths. The findings from this study contribute to the understanding of bias in recommender systems. These insights inform the development of strategies to mitigate biases and improve the user experience. Policymakers and platform developers can utilize this knowledge to establish effective guidelines and policies for their recommender systems, enhancing decision-making processes.

YouTube has revolutionized content consumption and global user interaction. It has become a central hub for video sharing, entertainment, and information dissemination. However, as the user base continues to expand and actively engage with the platform, concerns have arisen regarding the presence of suspicious behavior among commenters. This study presents an approach based on social network analysis to detect suspicious commenter behaviors and identify similarities across various YouTube channels in relation to such behaviors. The analysis involves 20 YouTube channels that disseminated false views about the U.S. Military. The dataset included 7,782 videos, 294,199 commenters, and 596,982 comments. We employ a combination of methods, including Graph2vec, UMAP, K-means, Hierarchical clustering, qualitative and quantitative analyses. The objective is to categorize channels based on the level of suspicious behavior and reveal common patterns exhibited among them. To assess the effectiveness of the proposed methodology, the outcomes revealed the presence of commenter mobs and significant similarities among these channels, providing valuable insights into the prevalence of suspicious commenter behavior.

YouTube Content Discovery Bot (YTCDB) is a cutting-edge multi-agent system designed to revolutionize the video discovery process. Traditionally, researchers have faced the arduous task of manually sorting through YouTube videos to find relevant content. YTCDB leverages an analytics-driven approach to autonomously locate videos given a seed video. Each task or process within YTCDB, such as comment scraping, gathering statistics, and collecting channel data, can be efficiently handled by one or multiple agents working in tandem. This distributed approach allows for seamless coordination and delegation of tasks, ensuring optimal performance and scalability. Insights gathered from video barcoding and content analysis of the video metadata and transcripts from the initial round of video discovery are used to propagate further exploration and data collection. This feedback loop refines the search criteria to provide a focused search through the endless content on YouTube. This multi-agent system represents a significant advancement in analytics-driven video discovery and in facilitating efficient data collection, analysis, and knowledge extraction in a time-efficient manner.

The initial announcement for the fourteenth ASONAM Conference invited the submission of research papers and special sessions proposals to the Social Networks Analysis and Mining (ASONAM 2022), Hague, Netherlands / 3-6 August 2022. However, the coronavirus epidemic is still affecting every human activity that included gatherings of people and travel is still limited for international conferences for many researchers. It was therefore decided to move the conference to a blended conference later in the 2022 and change the conference to a hybrid in-person conference and on-line conference at a different location. The final announcement was therefore ASONAM 2022-The 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 10-13 November 2022, Istanbul and Virtual on Zoom.

The advent of web 2.0 and social media blogging has enabled researchers to have access to troves of blog data in the 21stcentury. While platforms and big corporations like Twitter and Facebook can apply the concept of user networks by leveraging internal tools developed by their teams. Researchers and analysts have had to make use of repetitive ways of analyzing blog networks for collected data. This is due to the limited live databases to store and keep track of blog data and the lack of centralized publicly available tools with such capability. When analyzing blog data, the analyst often wants the capability to model relationships and see blogs that share ideological similarities. This is so because blogs always reference each other when they share similarities in content or when they attempt to reinforce a point of view discussed on the medium. Since the blogosphere is made up of a virtual network of blogs - the blogosphere is defined as the network of blogs and has no limitation in blogs referencing one another. It becomes imperative to have a solution that can allow an analyst to visualize the relationships between blogs based on how influential these blogs are when the analyst tracks the discus on the blogs. We address this by providing users with the capability to visualize and analyze blogs that are influential and how connected these blogs are by a way of network visualization. This demonstration shows how the BlogTracker application analyze and visualizes the blog network.

Blogging has become an essential part of the new print media of the 21st century despite the emergence of social media platforms like Twitter and Facebook, with many news agencies, media outlets, journalists and users using this medium to write without any restriction on topics of choice or events that happen over the world. Although social networking sites have also become a hotbed where users share their views, it suffers from distraction when users try to air their views on topics that affect them due to character limitation, real-time toxic behavior, and content ownership rights. Social networking sites like Facebook, Twitter and Reddit are sometimes used to drive traffic to blogs sites. The blogosphere, defined as the network of blogs, is growing at an exponential rate. Medium.com and WordPress.com are among the top blogging platforms, with WordPress leading the way as a top blogging platform and followed by other platforms like Medium, Hashnode, Tumblr, and blogger. Analyzing blog data helps understand the pulse of a society, know what resonates with a community, and recognize the grievances of a group, among other reasons. Since there is no character limit in blogs, unlike Twitter, blogs allow much depth in discourse, allowing it to be an effective platform for setting narratives. Blogs also provide a convenient platform to develop situational awareness during a socio-political crisis or humanitarian crisis in a conflict-torn region or a disaster-struck area. To address the difficulty of having a publicly accessible blog data analytical solution since solutions like Blogdex, among others, were either discontinued or made proprietary, we present BlogTracker. This tool helps users analyze public discussions with real-time data update capability and analyze narratives and emotion distribution on associated blog posts and trackers. This demonstration shows how the BlogTracker application analyses blog data with a case study about COVID-19.

YouTube is the second most popular website on the internet and a major actor in information propagation, therefore making it efficient as a potential vehicle of misinformation. Current tools available for video platforms tend to hyperfocus on metadata aggregation and neglect the analysis of the actual videos. In an attempt to provide analysts the tools they need to perform various research (behavioral, political analysis, sociology,etc.), we present VTracker (formerly YouTubeTracker), an online analytical tool. Some of the insight analysts can derive from this tool are inorganic behavior detection and algorithmic manipulation. We aim to make the analysis of YouTube content and user behavior accessible not only to information scientists but also communication researchers, journalists, sociologists, and many more. We demonstrate the utility of the tool through some real world data samples.

Complexity and dynamicity of the social networks are categorized as NP-hard problems to solve and analyze. These variables on social networks such as actions and interrelationships between the network's users, different behaviors, users' feedbacks and the networks' dynamics make them intractable. Systems thinking and modeling methods orient the relationships between all parts in online social networks. Complexity theories, system dynamics, and game theoretic approaches implemented by system thinkers help investigate the system's local parties' relationships. These methods also present a useful tool to interpret the social networks' complex interactions, dynamic activities in online networks and communities between users and their online dynamic interactions. In this paper, systems thinking concepts and organizational cybernetics are employed to analyze the armed protest demonstration against COVID-19 lockdown at Michigan capitol on May 12th through May 15th on Twitter. Utilizing these methods, we also present a systematic analysis to control, analyze, and comprehend the actions, tweets, and retweets exchanged between users' supporting and opposing the campaign in a complex and dynamic environment.

YouTube is the second most popular website in the world. Over 300 hours worth of videos are uploaded every minute and 5 billion videos are watched every day - almost one video per person worldwide. Because videos can deliver a complex message in a way that captures the audience's attention more effectively than text-based platforms, it has become one of the most relevant platforms in the age of digital mass communication. This makes the analysis of YouTube content and user behavior invaluable not only to information scientists but also communication researchers, journalists, sociologists, and many more. There exists a number of YouTube analysis tools but none of them provide an in-depth qualitative and quantitative insights into user behavior or networks. Towards that direction, we introduce YouTubeTracker - a tool designed to gather YouTube data and gain insights on content and users. This tool can help identify leading actors, networks and spheres of influence, emerging popular trends, as well as user opinion. This analysis can also be used to understand user engagement and social networks. This can help reveal suspicious and inorganic behaviors (e.g., trolling, botting) causing algorithmic manipulations.

Social media has grown to be the place for voicing one's opinions, sharing information, and shaping discourse. Individuals use social media as a platform to mobilize, coordinate, and conduct cyber campaigns ranging from awareness for diseases or disorders to deviant acts threatening democratic principles and institutions. Blogosphere has continued to rise and afford an effective medium for content framing. With no restriction on the number of characters, many use blogs to set narratives then use other social media channels like Twitter and Facebook to steer their audience to their blogs. Blog content is not structured and hard to collect than other social media channels. Blog monitoring and analysis could be of great use to sociologists, political scientists, communication researchers, journalists, and information scientists to examine events. Toward this direction, we present Blogtrackers tool, which is designed to explore the blogosphere and gain insights on various events. Blogtrackers can help in identifying leading information actors, influential bloggers, popular and emerging trends assess tones, sentiments and opinions, extract entities, and analyze their networks.

YouTube, since its inception in 2005, has grown to become largest online video sharing website. It's massive userbase uploads videos and generates discussion by commenting on these videos. Lately, YouTube, akin to other social media sites, has become a vehicle for spreading fake news, propaganda, conspiracy theories, and radicalizing content. However, lack ineffective image and video processing techniques has hindered research on YouTube. In this paper, we advocate the use of metadata in identifying such malicious behaviors. Specifically, we analyze metadata of videos (e.g., comments, commenters) to study a channel on YouTube that was pushing content promoting conspiracy theories regarding World War III. Identifying signals that could be used to detect such deviant content (e.g., videos, comments) can help in stemming the spread of disinformation. We collected over 4,145 videos along with 16,493 comments from YouTube. We analyze user engagement to assess the reach of the channel and apply social network analysis techniques to identify inorganic behaviors.

Recently, several journalistic accounts have suggested that Twitter is becoming a bellwether for mis- and disinformation due to the pervasiveness of bots. These bots are either automated or semi-automated. Understanding the intent and usage of these bots has piqued the scientific curiosity among researchers. To that effect, in this study, we analyze the role of bots in two distinct categories of real-world events, i.e., natural disasters and sports. We collected over 1.2 million tweets that were generated by nearly 800,000 users for Hurricane Harvey, Hurricane Irma, Hurricane Maria, and Mexico Earthquake. We corroborate our analysis by examining bots that engaged with the 2018 Winter Olympics. We collected over 1.4 million tweets generated by nearly 700,000 users based on the hashtags #Olympics2018 and #PyeongChang2018. We examined the social and communication network of bots and humans for the aforementioned events. Our results show distinctive patterns in the network structures of bots when compared with that of humans. Content analysis of the tweets further revealed that bots used hashtags more uniformly than humans, across all the events.

Automated social bots are reported to account for a large sum of activity on social media sites such as Twitter. In this short paper, we study the information-foraging behaviors of social media users including bots. We present here a preliminary investigation which compares the behaviors of a set of suspected bots with non-automated accounts. To do so, we measure the distance between word distributions on a daily basis. We posit that this methodology provides a quantitative measure of behavior, which allows for more rigorous descriptions of bot behaviors that move beyond the assumption of bots as a monolithic category.

Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research. It helps us infer the causality between two seemingly unrelated set of events. For example, if consumer spending in the United States declines, it has spillover effects on economies that depend on the U.S. as their largest export market. In this paper, we aim to infer the causation that results in spillover effects between pairs of entities (or units); we call this effect as paired spillover. To achieve this, we leverage the recent developments in variational inference and deep learning techniques to propose a generative model called Linked Causal Variational Autoencoder (LCVA). Similar to variational autoencoders (VAE), LCVA incorporates an encoder neural network to learn the latent attributes and a decoder network to reconstruct the inputs. However, unlike VAE, LCVA treats the latent attributes as confounders that are assumed to affect both the treatment and the outcome of units. Specifically, given a pair of units u and $\baru $, their individual treatment and outcomes, the encoder network of LCVA samples the confounders by conditioning on the observed covariates of u, the treatments of both u and $\baru $ and the outcome of u. Once inferred, the latent attributes (or confounders) of u captures the spillover effect of $\baru $ on u. Using a network of users from job training dataset (LaLonde (1986)) and co-purchase dataset from Amazon e-commerce domain, we show that LCVA is significantly more robust than existing methods in capturing spillover effects.

In addition to using social media to connect with others worldwide, many people nowadays get their news about different national or international events such as natural disasters, crises, political elections, conflicts etc. via social media. This evolution in the usage of social media has not only led to the generation of massive amounts of data but also various information consumption behaviors. In this study, we developed a framework that can be used to monitor/understand, analyze, and visualize in real time how people consume information and react to events. Following the case study of Apple, Inc. vs. FBI, we tracked the usage of the #NoBackDoor on Twitter in real time and were able to understand what people are thinking about the case and who are the actors involved in this network. The framework can be applied to study other events and provide a deeper understanding of how public sentiments evolve during an event, whether it is a crisis or major news event.

The advent of modern forms of information and communication technologies (ICTs), such as social media, has modified the ways people communicate and enables an inimitable way of connectivity promoting the advancement and diffusion of information. Given that, individuals within social movements bringing influence from other movements makes the study of spillover within social movements an important concentration for sociologists and others studying collective action (CA). This research explores the role of social movement spillover in investigating and sustaining the Women's Right to Drive movement in the Kingdom of Saudi Arabia. We benefit from various models of established collective action theories developed in the pre-Internet era, and re-evaluate traditional theories of spillover within the modern ICT landscape by utilizing existing collective action theories/approaches and novel and innovative computational analytical tools. The findings of this study are conceptualized to shed new insights on information diffusion, mutual influence, role distribution analysis of activists/supporters across movements and provide a deeper understanding of interconnected social movements and social movement spillover. Applying computational metrics to measure the strength of spillover and its effects on complex social processes enable novel model development to help advance the understanding of interconnected collective actions conducted through modern social and information systems.

With an increasing number of kids diagnosed with Autism Spectrum Disorder (ASD), countries face a shortage of resources for efficient delivery of autism support. Social media sites provide open and accessible communication platforms for families, caregivers, and individuals with autism to share, connect, and exchange information with others. We systematically analyze these interactions on blogs and Twitter among families and autism communities to extract knowledge and develop practical and useful learning tools. The study found that the autism community provides significant social support to its members. Social support facilitated by community members can help caregivers surmount challenges and be effective in reducing psychological stress and enhancing the quality of life for individuals diagnosed with ASD.

In recent mass protests such as the Arab Spring and Occupy movements, protesters used social media to spread awareness, coordinate, and mobilize support. Social media-assisted collective action has attracted much attention from journalists, political observers, and researchers of various disciplines. In this article, the authors study transnational online collective action through the lens of inter-network cooperation. The authors analyze interaction and support between the women's rights networks of two online collective actions: ‘Women to Drive' (primarily Saudi Arabia) and ‘Sexual Harassment' (global). Methodologies used include: extracting each collective action's social network from blogs authored by female Muslim bloggers (23 countries), mapping interactions among network actors, and conducting sentiment analysis on observed interactions to provide a better understanding of inter-network support. The authors examine these two distinct but overlapped networks of collective actions and discover that brokering and bridging processes can facilitate the diffusion of information, coalition formation, and the expansion of the networks. The broader goal of the study is to examine the dynamics between interconnected collective actions. This research contributes to understanding the mobilization of social movements in digital activism and the role of cooperative networks in online collective action.

Social media is inarguably a powerful medium for mobilizing support for various real-life events be it for social, political, or economic transformation. Further, in contrast to the generic information obtained from the mainstream media, novel and specific information available at social media sites makes them valuable sources for event analysis. However, due to the power law distribution of the Internet, these overwhelmingly large number of sources are buried in the Long Tail making it extremely challenging to identify the quality sources among them. In this research, we propose an evolutionary mutual reinforcement model to confront these challenges. Due to absence of ground truth, a novel evaluation strategy is introduced. The results indicate tremendous potential. 25% to 130% information gain is obtained with the proposed approach when compared against the state-of-the-art baselines, viz. Google blog search and Icerocket blog search. Further, our ranking methodology is capable of identifying the highly informative sources much earlier than the aforementioned baselines. The proposed model affords an apparatus for micro and macro event analysis.

Gene selection aims at detecting biologically relevant genes to assist biologists' research. The cDNA Microarray data used in gene selection is usually “wide”. With more than several thousand genes, but only less than a hundred of samples, many biologically irrelevant genes can gain their statistical relevance by sheer randomness. Addressing this problem goes beyond what the cDNA Microarray can offer and necessitates the use of additional information. Recent developments in bioinformatics have made various knowledge sources available, such as the KEGG pathway repository and Gene Ontology database. Integrating different types of knowledge could provide more information about genes and samples. In this work, we propose a novel approach to integrate different types of knowledge for identifying biologically relevant genes. The approach converts different types of external knowledge to its internal knowledge, which can be used to rank genes. Upon obtaining the ranking lists, it aggregates them via a probabilistic model and generates a final list. Experimental results from our study on acute lymphoblastic leukemia demonstrate the efficacy of the proposed approach and show that using different types of knowledge together can help detect biologically relevant genes.

Similarity breeds connections, the principle of homophily, has been well studied in existing sociology literature. %Several studies have observed this phenomena by conducting surveys on human subjects. These studies have concluded that new ties are formed between similar individuals. This phenomenon has been used to explain several socio-psychological concepts such as segregation, community development, social mobility, etc. However, due to the nature of these studies and limitations because of involvement of human subjects, conclusions from these studies are not easily extensible in online social media. %Social media, which is becoming the infinite space for interactions, has exceeded all the expectations in terms of growth, for reasons beyond human mind. New ties are formed in social media just like the way they emerge in real-world. However, given the differences between real-world and online social media, do the same factors that govern the construction of new ties in real-world also govern the construction of new ties in social media? In other words, does homophily exist in social media? In this article, we study this extremely significant question. We propose a systematic approach by studying two online social media sites, BlogCatalog and Last.fm and report our findings along with some interesting observations.

The nature of the Blogosphere determines that the majority of bloggers are only connected with a small number of fellow bloggers, and similar bloggers can be largely disconnected from each other. Aggregating them allows for cost-effective personalized services, targeted marketing, and exploration of new business opportunities. As most bloggers have only a small number of adjacent bloggers, the problem of aggregating similar bloggers presents challenges that demand novel algorithms of connecting the non-adjacent due to the fragmented distributions of bloggers. In this work, we define the problem, delineate its challenges, and present an approach that uses innovative ways to employ contextual information and collective wisdom to aggregate similar bloggers. A real-world blog directory is used for experiments. We demonstrate the efficacy of our approach, report findings, and discuss related issues and future work.

Blogosphere is expanding in an unprecedented speed. A better understanding of the blogosphere can greatly facilitate the development of the Social Web to serve the needs of users, service providers and advertisers. One important task in this process is clustering blog sites. Clustering blog sites presents new challenges. We propose to tap into collective wisdom in clustering blog sites, present statistical and visual results, report findings, and suggest future work extending to many real-world applications.

Blogging becomes a popular way for a Web user to publish information on the Web. Bloggers write blog posts, share their likes and dislikes, voice their opinions, provide suggestions, report news, and form groups in Blogosphere. Bloggers form their virtual communities of similar interests. Activities happened in Blogosphere affect the external world. One way to understand the development on Blogosphere is to find influential blog sites. There are many non-influential blog sites which form the "the long tail". Regardless of a blog site being influential or not, there are influential bloggers. Inspired by the high impact of the influentials in a physical community, we study a novel problem of identifying influential bloggers at a blog site. Active bloggers are not necessarily influential. Influential bloggers can impact fellow bloggers in various ways. In this paper, we discuss the challenges of identifying influential bloggers, investigate what constitutes influential bloggers, present a preliminary model attempting to quantify an influential blogger, and pave the way for building a robust model that allows for finding various types of the influentials. To illustrate these issues, we conduct experiments with data from a real-world blog site, evaluate multi-facets of the problem of identifying influential bloggers, and discuss unique challenges. We conclude with interesting findings and future work

A topic taxonomy is an effective representation that describes salient features of virtual groups or online communities. A topic taxonomy consists of topic nodes. Each internal node is defined by its vertical path (i.e., ancestor and child nodes) and its horizonal list of attributes (or terms). In a text-dominant environment, a topic taxonomy can be used to flexibly describe a group's interests with varying granularity. However, the stagnant nature of a taxonomy may fail to timely capture the dynamic change of a group's interest. This article addresses the problem of how to adapt a topic taxonomy to the accumulated data that reflects the change of a group's interest to achieve dynamic group profiling. We first discuss the issues related to topic taxonomy. We next formulate taxonomy adaptation as an optimization problem to find the taxonomy that best fits the data. We then present a viable algorithm that can efficiently accomplish taxonomy adaptation. We conduct extensive experiments to evaluate our approach's efficacy for group profiling, compare the approach with some alternatives, and study its performance for dynamic group profiling. While pointing out various applications of taxonomy adaption, we suggest some future work that can take advantage of burgeoning Web 2.0 services for online targeted marketing, counterterrorism in connecting dots, and community tracking.

There is an increasing need for an XML query engine that not only searches for exact matches to a query but also returns "query-like" structures. We have designed and developed XFinder, an efficient top K tree pattern query evaluation system, which reduces the problem of approximate tree structural matching to a simpler problem of subsequence matching. However, since not all subsequences correspond to valid tree structures, it is expensive to enumerate common subsequences between XML data and query and then filter the invalid ones. XFinder addresses this challenge by detecting and pruning structurally irrelevant subsequence matches as early as possible. Experiments show the efficiency of XFinder on various data and query sets.