When Life Paths Cross: Extracting Human Interactions in Time and Space from Wikipedia

• URL: http://arxiv.org/abs/2510.00019v1
• Date: Mon, 22 Sep 2025 11:42:24 GMT
• Title: When Life Paths Cross: Extracting Human Interactions in Time and Space from Wikipedia
• Abstract summary: We mine Wikipedia for interaction records in the form of (Person1, Person2, Time, Location) interaction quadruplets.<n>Key elements of these interactions are often scattered throughout the heterogeneous crowd-sourced text.<n>We design a model that integrates attention mechanisms, multi-task learning, and feature transfer methods, achieving an F1 score of 86.51%.
• Score: 7.088308741097303
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Interactions among notable individuals -- whether examined individually, in groups, or as networks -- often convey significant messages across cultural, economic, political, scientific, and historical perspectives. By analyzing the times and locations of these interactions, we can observe how dynamics unfold across regions over time. However, relevant studies are often constrained by data scarcity, particularly concerning the availability of specific location and time information. To address this issue, we mine millions of biography pages from Wikipedia, extracting 685,966 interaction records in the form of (Person1, Person2, Time, Location) interaction quadruplets. The key elements of these interactions are often scattered throughout the heterogeneous crowd-sourced text and may be loosely or indirectly associated. We overcome this challenge by designing a model that integrates attention mechanisms, multi-task learning, and feature transfer methods, achieving an F1 score of 86.51%, which outperforms baseline models. We further conduct an empirical analysis of intra- and inter-party interactions among political figures to examine political polarization in the US, showcasing the potential of the extracted data from a perspective that may not be possible without this data. We make our code, the extracted interaction data, and the WikiInteraction dataset of 4,507 labeled interaction quadruplets publicly available.

Related papers

• Learning Human-Object Interaction as Groups [52.28258599873394]
  GroupHOI is a framework that propagates contextual information in terms of geometric proximity and semantic similarity.<n>It exhibits leading performance on the more challenging Nonverbal Interaction Detection task.
  arXiv  Detail & Related papers  (2025-10-21T07:25:10Z)
• Explaining Categorical Feature Interactions Using Graph Covariance and LLMs [18.44675735926458]
  This paper focuses on the global synthetic dataset from the Counter Trafficking Data Collaborative.<n>It contains over 200,000 anonymized records spanning from 2002 to 2022 with numerous categorical features for each record.<n>We propose a fast and scalable method for analyzing and extracting significant categorical feature interactions.
  arXiv  Detail & Related papers  (2025-01-24T21:41:26Z)
• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• Inter-X: Towards Versatile Human-Human Interaction Analysis [100.254438708001]
  We propose Inter-X, a dataset with accurate body movements and diverse interaction patterns. The dataset includes 11K interaction sequences and more than 8.1M frames. We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions.
  arXiv  Detail & Related papers  (2023-12-26T13:36:05Z)
• MMoE: Enhancing Multimodal Models with Mixtures of Multimodal Interaction Experts [92.76662894585809]
  We introduce an approach to enhance multimodal models, which we call Multimodal Mixtures of Experts (MMoE) MMoE is able to be applied to various types of models to gain improvement.
  arXiv  Detail & Related papers  (2023-11-16T05:31:21Z)
• Interactions in Information Spread [0.0]
  We study the role of information interaction in social networks. We find that interactions are rare in several social networks. We design a framework that jointly models rare and brief interactions. We conduct a large-scale application on Reddit and find that interactions play a minor role in this dataset.
  arXiv  Detail & Related papers  (2022-09-16T16:11:40Z)
• Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey [71.43956423427397]
  We aim to identify the nonverbal cues and computational methodologies resulting in effective performance. This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings. Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
  arXiv  Detail & Related papers  (2022-07-20T13:37:57Z)
• Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition [55.7731053128204]
  In given video-based Human-Object Interaction scene, modeling thetemporal relationship between humans and objects are the important cue to understand the contextual information presented in the video. With the effective-temporal relationship modeling, it is possible not only to uncover contextual information in each frame but also directly capture inter-time dependencies. The full use of appearance features, spatial location and the semantic information are also the key to improve the video-based Human-Object Interaction recognition performance.
  arXiv  Detail & Related papers  (2021-08-19T11:57:27Z)
• Evaluating the state-of-the-art in mapping research spaces: a Brazilian case study [0.0]
  Two recent works propose methods for creating research maps from scientists' publication records. We evaluate these models' ability to predict whether a given entity will enter a new field. We conduct a case study to showcase how these models can be used to characterize science dynamics in the context of Brazil.
  arXiv  Detail & Related papers  (2021-04-07T18:14:41Z)
• Continuous Latent Position Models for Instantaneous Interactions [0.0]
  We create a framework to analyse the timing and frequency of instantaneous interactions between pairs of entities. Examples of instantaneous interactions include email networks, phone call networks and some common types of technological and transportation networks.
  arXiv  Detail & Related papers  (2021-03-31T15:10:58Z)
• Human Trajectory Forecasting in Crowds: A Deep Learning Perspective [89.4600982169]
  We present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. We develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting.
  arXiv  Detail & Related papers  (2020-07-07T17:19:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.