Event Identification in Social Networks

@article{Zarrinkalam2017EventII,
  title={Event Identification in Social Networks},
  author={Fattane Zarrinkalam and Ebrahim Bagheri},
  journal={ArXiv},
  year={2017},
  volume={abs/1606.08521}
}
Social networks enable users to freely communicate with each other and share their recent news, ongoing activities or views about different topics. As a result, they can be seen as a potentially viable source of information to understand the current emerging topics/events. The ability to model emerging topics is a substantial step to monitor and summarize the information originating from social sources. Applying traditional methods for event detection which are often proposed for processing… 
View PDF on arXiv
14 Citations
Highly Influential Citations
1
Background Citations
6
Methods Citations
2

14 Citations

Citation Type

Citation Type

High‐level event identification in social media
TLDR
This work proposes a new approach, which recognizes geo‐referenced high‐level events/activities mentioned in web sources adopting open gazetteers: OpenStreetMap and Google Maps, and identifies events associated with the relevant topics using a latent Dirichlet allocation.
Trending Topic Extraction using Topic Models and Biterm Discrimination
TLDR
This research aims to determine if it is possible to reduce the amount of processing required and getting equally good results by a discrimination of co-occurrences of words (biterms) used by BBTM to model trending topics.
SECURITY ASPECTS IN SOCIAL NETWORKING MODEL
TLDR
A model, organized around Entity-Relationship paradigm and experts’ knowledge for the problem, is presented and validated on the basis of psychophysiological monitoring amongst two focus groups, which have shown a predisposition to Web 2.0 technological threats by means of manipulative social networking, concerning the users and over trust in some famous social networks.
An Event-local View: Emotion Interplay in the Underlying Social Graph of a Literary Text
TLDR
This work delivers an integrated visualization to display the emotion interplay between main protagonists in the underlying social network in an event-specific manner in contrast to the traditional static global views of the social graph made by analyzing the full text.
Building socially-enabled event-enriched maps
TLDR
This paper introduces Hadath, a scalable and efficient system that extracts social events from unstructured data streams, e.g. Twitter, and applies natural language processing and multi-dimensional clustering techniques to extract relevant events of interest at different map scales, and to infer the spatio-temporal scope of detected events.
A Survey on Event Detection Models for Text Data Streams
TLDR
In this survey, ED models for text data from various Social Network sites (SNs) are analyzed based on domain type, detection methods, type of detection task and the major open challenges faced by researchers for building ED models are explained and discussed in detail.
A sparse topic model for bursty topic discovery in social networks
TLDR
A Sparse Topic Model (STM) for bursty topic discovery is proposed that outperforms favorably against several state-of-the-art methods and introduces "Spike and Slab" prior to decouple the sparsity and smoothness of a distribution.
Temporally Like-minded User Community Identification through Neural Embeddings
TLDR
A neural embedding approach to identify temporally like-minded user communities, i.e., those communities of users who have similar temporal alignment in their topics of interest, by leveraging unsupervised feature learning (embeddings).
Extracting, Mining and Predicting Users' Interests from Social Media
TLDR
This monograph will cover the foundations of social user interest modeling, such as information sources, various types of representation models and temporal features, and techniques that have been adopted or proposed for Fattane Zarrinkalam, Stefano Faralli, Guangyuan Piao and Ebrahim Bagheri (2020).
Analysing and Predicting Propaganda on Social Media using Machine Learning Techniques
TLDR
Propaganda is one of the Systematic and purposeful endeavors utilized for impacting individuals for the political and religious gains and support vector machine showed the better results among all other traditional machine learning algorithms.
...
...

References

SHOWING 1-10 OF 77 REFERENCES
Event Detection and Tracking in Social Streams
TLDR
A new event detection algorithm is proposed and developed which creates a keyword graph and uses community detection methods similar to those used for social network analysis to dis- cover and describe events.
Semantics-Enabled User Interest Detection from Twitter
TLDR
Each topic of interest is viewed as a conjunction of several concepts, which are temporally correlated on Twitter, and this work extracts active topics within a given time interval and determines a users inclination towards these active topics.
Event Discovery in Social Media Feeds
TLDR
A graphical model is developed that addresses record extraction from social streams such as Twitter by learning a latent set of records and a record-message alignment simultaneously, resulting in a set of canonical records that are consistent with aligned messages.
Event detection and trending in multiple social networking sites
TLDR
A novel approach of discovering events from multiple social streams using widely used Euclidean realization of locality sensitive hashing (LSH) algorithm for event detection and trending in multiple social sites is suggested.
Emerging topic detection on Twitter based on temporal and social terms evaluation
TLDR
A novel topic detection technique that permits to retrieve in real-time the most emergent topics expressed by the community, under user-specified time constraints is proposed.
Selecting Quality Twitter Content for Events
TLDR
This paper explores approaches for finding representative messages among a set of Twitter messages that correspond to the same event, with the goal of identifying high quality, relevant messages that provide useful event information.
Sensing Trending Topics in Twitter
TLDR
It is found that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel.
Clustering memes in social media streams
TLDR
A streaming framework for online detection and clustering of memes in social media, specifically Twitter, that outperforms baseline algorithms that only use content features, as well as a state-of-the-art event detection method that assumes full knowledge of the underlying follower network.
Streaming First Story Detection with application to Twitter
TLDR
This work presents an algorithm based on locality-sensitive hashing which is able to overcome the limitations of traditional approaches, while maintaining competitive results in event detection on web-scale corpora.
Breaking News Detection and Tracking in Twitter
  • S. Phuvipadawat, T. Murata
  • Computer Science
    2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology
  • 2010
TLDR
An application called “Hotstream” is developed based on the proposed method to collect, group, rank and track breaking news in Twitter, and boosts scores on proper nouns to improve the grouping results.
...
...