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.

Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based and data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamic environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, improving the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (~12 billion tweets, 2.5 million subreddit data, and 700k news articles) and multiple knowledge graphs. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92%. This approach also showed faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models (LLMs). This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance.

The digital landscape continually evolves with multimodality, enriching the online experience for users. Creators and marketers aim to weave subtle contextual cues from various modalities into congruent content to engage users with a harmonious message. This interplay of multimodal cues is often a crucial factor in attracting users’ attention. However, this richness of multimodality presents a challenge to computational modeling, as the semantic contextual cues spanning across modalities need to be unified to capture the true holistic meaning of the multimodal content. This contextual meaning is critical in attracting user engagement as it conveys the intended message of the brand or the organization. In this work, we incorporate external commonsense knowledge from knowledge graphs to enhance the representation of multimodal data using compact Visual Language Models (VLMs) and predict the success of multi-modal crowdfunding campaigns. Our results show that external knowledge commonsense bridges the semantic gap between text and image modalities, and the enhanced knowledge-infused representations improve the predictive performance of models for campaign success upon the baselines without knowledge. Our findings highlight the significance of contextual congruence in online multimodal content for engaging and successful crowdfunding campaigns.

Imprecise composite location references formed using ad hoc spatial expressions in English text makes the geocoding task challenging for both inference and evaluation. Typically such spatial expressions fill in unestablished areas with new toponyms for finer spatial referents. For example, the spatial extent of the ad hoc spatial expression "north of" or "50 minutes away from" in relation to the toponym "Dayton, OH" refers to an ambiguous, imprecise area, requiring translation from this qualitative representation to a quantitative one with precise semantics using systems such as WGS84. Here we highlight the challenges of geocoding such referents and propose a general formal representation that employs background knowledge, semantic approximations and rules, and fuzzy linguistic variables. We also discuss an appropriate evaluation technique for the task that is based on human contextualized and subjective judgment.

Natural disasters such as floods, forest fires, and hurricanes can cause catastrophic damage to human life and infrastructure. We focus on response to hurricanes caused by both river water flooding and storm surge. Using models for storm surge simulation and flood extent prediction, we generate forecasts about areas likely to be highly affected by the disaster. Further, we overlay the simulation results with information about traffic incidents to correlate traffic incidents with other data modality. We present these results in a modularized, interactive map-based visualization, which can help emergency responders to better plan and coordinate disaster response.

Recent studies show that by combining network topology and node attributes, we can better understand community structures in complex networks. However, existing algorithms do not explore "contextually" similar node attribute values, and therefore may miss communities defined with abstract concepts. We propose a community detection and characterization algorithm that incorporates the contextual information of node attributes described by multiple domain-specific hierarchical concept graphs. The core problem is to find the context that can best summarize the nodes in communities, while also discovering communities aligned with the context summarizing communities. We formulate the two intertwined problems, optimal community-context computation, and community discovery, with a coordinate-ascent based algorithm that iteratively updates the nodes' community label assignment with a community-context and computes the best context summarizing nodes of each community. Our unique contributions include (1) a composite metric on Informativeness and Purity criteria in searching for the best context summarizing nodes of a community; (2) a node similarity measure that incorporates the context-level similarity on multiple node attributes; and (3) an integrated algorithm that drives community structure discovery by appropriately weighing edges. Experimental results on public datasets show nearly 20 percent improvement on F-measure and Jaccard for discovering underlying community structure over the current state-of-the-art of community detection methods. Community structure characterization was also accurate to find appropriate community types for four datasets.

A crowd sampled from a set of individuals can provide a more accurate prediction in aggregate than most individuals.This effect, referred to as wisdom of crowd, exists when crowd members bring diverse perspectives to decision making. Such diversity leads to uncorrelated prediction errors that cancel out in aggregate. As crowd members' judgments are often the result of solution strategies, diversity in solution strategies can enhance crowd wisdom. One of the most challenging tasks in sampling such a crowd is to determine the individual's solution strategy for a prediction problem. As participating individuals often share their perspectives through social media, we can use such data to identify an individual's solution strategy. In this paper, we propose a crowd selection approach using social media posts (tweets) indicating diverse solution strategies. We use tweet classification to identify participants' prediction strategies and categorize participants based on the binomial test to identify sets of participants that apply a similar strategy. We then form a diverse crowd by sampling participants from different sets. Using the domain of Fantasy Sports, we show that such a diverse crowd can outperform crowd selected at random and 90% of individual participants, and participant categorization schemes using word2vec. Further, we use a knowledge graph to investigate the factors forming such a diverse crowd and how these factors can lead to a better decision. Relative to bottom-up (data-driven) processes the approach presented here provides an explanation of diverse crowd behavior.

"Wisdom of Crowds" (WoC) refers to a form of collective intelligence in which the aggregate judgment of a group of individuals is, in most instances, superior to that of any one group member. For a crowd to be wise, its members must possess diverse knowledge and viewpoints. Such diversity leads to uncorrelated judgment errors that cancel out in aggregate. Yet despite the fact that diversity is known to be an essential ingredient in WoC, little research aims to measure and exploit diversity in human social systems for the purpose of maximizing crowd intelligence. Here we quantify the diversity of a group of individuals through semantic analysis of their social media (Twitter) communications. Focusing on the domain of fantasy sports, we show that virtual crowds of fantasy team owners selected based on the diversity of their tweet content can outperform both non-diverse and randomly sampled crowds. Our results suggest a new approach for intelligent crowd assembly in which measures of diversity extracted from online social media communications can guide the selection of crowd members. These results have implications for numerous domains that utilize aggregated judgments - from consumer reviews, to econometrics, to geopolitical forecasting and intelligence analysis.