Event detection and visualization for social text streams (original) (raw)

Temporal and information flow based event detection from social text streams

2007

Recently, social text streams (e.g., blogs, web forums, and emails) have become ubiquitous with the evolution of the web. In some sense, social text streams are sensors of the real world. Often, it is desirable to extract real world events from the social text streams. However, existing event detection research mainly focused only on the stream properties of social text streams but ignored the contextual, temporal, and social information embedded in the streams. In this paper, we propose to detect events from social text streams by exploring the content as well as the temporal, and social dimensions. We define the term event as the information flow between a group of social actors on a specific topic over a certain time period. We represent social text streams as multi-graphs, where each node represents a social actor and each edge represents the information flow between two actors. The content and temporal associations within the flow of information are embedded in the corresponding edge. Events are detected by combining text-based clustering, temporal segmentation, and information flow-based graph cuts of the dual graph of the social networks. Experiments conducted with the Enron email dataset 1 and the political blog dataset from Dailykos 2 show the proposed event detection approach outperforms the other alternatives.

Parametrized Event Analysis from Social Networks

Scientific Journal of Astana IT University

The growth of data in social networks facilitate demand for data analysis. The field of event detection is of increasing interest to researchers. Events from real life are actively discussed in the virtual space. Event detection results can be used in a variety of applications, from digital marketing to collecting data about natural disasters. Thereby, researchers face the emergence of new algorithms along with the improvement of existing solutions in the event detection field. This paper proposes improvements to the SEDTWik (Segment-based Event Detection from Tweets using Wikipedia) algorithm. The SEDTWik algorithm is designed to detect events without contextual guidance. The overall SEDTWik detection process excludes the perspective of a topic, or multi-topic, guided (or semi-supervised) event detection approach. As a result, some interesting narrowly focused events are not detected as they are weakly relevant in a broader context (e.g., Wikipedia) although acquiring relevance wit...

Link-based event detection in email communication networks

Proceedings of the 2009 ACM symposium on Applied Computing - SAC '09, 2009

People's email communications can be modeled as graphs with vertices representing email accounts and edges representing email communications. Email communication data usually comes in as continuous data stream. Event detection aims to identify abnormal email communications that serve as analogs of real-world events imposed upon the data stream. The goal is to understand the communications behaviors of the subjects. The contents of emails are often not available or protected by privacy, which makes linkage information the only resource we can rely on. We propose a linkbased event detection method that clusters vertices with similar communication patterns together and then, considers deviations from each vertex's individual profile, as well as its cluster profile. Experiments show that this method performs well on both Enron and our own email datasets.

Leveraging Phase Transition of Topics for Event Detection in Social Media

IEEE Access, 2020

With the advancement of technology, many processes in our world have been reformulated, updated, and digitized. Therefore, interpersonal relationships have also been following this trend so that social networks have become increasingly present in our lives. Given this context, social network users create and share a large amount of data, from content about their daily lives, funny facts, as well as information about traffic, weather, and various subjects. The problem of event detection in social media, such as Twitter, is related to the identification of the first story on a topic of interest. In this work, we propose a novel approach based on the observation that tweets are subjected to a continuous phase transition when an event takes place, i.e., its underlying dynamic changes. Our proposal consists of a formal characterization of the phase transition that occurs when an event takes place, and the use of this characterization to devise a new method to detect events in Twitter, based on calculating the entropy of the keywords extracted from the content of tweets (regardless of the language used). We evaluated the performance of our approach using seven data sets, and we outperformed nine different techniques present in the literature. Unlike the work found in the literature, we present a theoretical rationale about the existence of phase transitions. For this, we characterize a model, already existing in the literature, of phase transitions described by differential equations, where we find correspondence between the model used in the study and the real data. The experimental results show that our proposal significantly improves the learning performance for the metrics used. INDEX TERMS Event detection, information-theoretic metrics, phase transition, social media analysis I. INTRODUCTION Social networks have become an essential venue for social communication, information sharing, and other activities. There are different types of Social networks serving various purposes, such as relationship networks (Facebook, Twitter, Instagram), professional networks (Linkedin, Classroom 2.0), multimedia sharing networks (Youtube, Flickr), among others. Such social networks typically produce rich amounts of user-generated information related to situation reports and can be massively used for different applications, for instance, data aggregation [1], source identification of rumors [2], social network analysis [3], recommendation systems [4], event stream dissemination [5], networks modeling [6]. The associate editor coordinating the review of this manuscript and approving it for publication was Santhosh Kumar Gopalan.

IJERT-A Comparative Study on Various Approaches for Event Detection in Social Streams

International Journal of Engineering Research and Technology (IJERT), 2015

https://www.ijert.org/a-comparative-study-on-various-approaches-for-event-detection-in-social-streams https://www.ijert.org/research/a-comparative-study-on-various-approaches-for-event-detection-in-social-streams-IJERTV4IS041296.pdf Social network sites (SNS) can be regarded as social sensors which can capture a number of events daily in the society. Ex: Face book, Twitter. SNS contain tremendous amount of text, image, audio or video content which can be leveraged for a wide range of business purposes. This paper explores a detailed comparative study about how to identify events in social streams. It also explained various event detection methods and algorithms.

A Comparative Study on Various Approaches for Event Detection in Social Streams

International Journal of Engineering Research and, 2015

Social network sites (SNS) can be regarded as social sensors which can capture a number of events daily in the society. Ex: Face book, Twitter. SNS contain tremendous amount of text, image, audio or video content which can be leveraged for a wide range of business purposes. This paper explores a detailed comparative study about how to identify events in social streams. It also explained various event detection methods and algorithms.

Tracing Topic Transitions with Temporal Graph Clusters

arXiv (Cornell University), 2021

Twitter serves as a data source for many Natural Language Processing (NLP) tasks. It can be challenging to identify topics on Twitter due to continuous updating data stream. In this paper, we present an unsupervised graph based framework to identify the evolution of sub-topics within two weeks of real-world Twitter data. We first employ a Markov Clustering Algorithm (MCL) with a node removal method to identify optimal graph clusters from temporal Graph-of-Words (GoW). Subsequently, we model the clustering transitions between the temporal graphs to identify the topic evolution. Finally, the transition flows generated from both computational approach and human annotations are compared to ensure the validity of our framework.

Event Detection via Tracking the Change in Community Structure and Communication Trends

IEEE Access

Event detection is a popular research problem aiming to detect events from various data sources, such as news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in community structure and communication trends. With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called community size range based change tracking is presented such that the proposed algorithms can focus on communities with different size ranges, and considerable time efficiency can be obtained. The event detection performance of the proposed methods is analyzed using a set of real world and benchmark data sets in comparison to previous solutions in the literature. The experiments show that the proposed methods have higher event detection accuracy than the baseline methods. Additionally, their scalability is presented through analysis by using high volume of communication data. Among the proposed methods, CN-NEW, which is a community structure based method, performs the best on the overall. The proposed communication trend based methods perform better mostly on communication data sets (such as CDR), whereas community structure based methods tend to perform better on social media-based data sets.

Generalized durative event detection on social media

Journal of Intelligent Information Systems

Given the recent availability of large volumes of social media discussions, finding temporal unusual phenomena, which can be called events, from such data is of great interest. Previous works on social media event detection either assume a specific type of event, or assume certain behavior of observed variables. In this paper, we propose a general method for event detection on social media that makes few assumptions. The main assumption we make is that when an event occurs, affected semantic aspects will behave differently from their usual behavior, for a sustained period. We generalize the representation of time units based on word embeddings of social media text, and propose an algorithm to detect durative events in time series in a general sense. In addition, we also provide an incremental version of the algorithm for the purpose of real-time detection. We test our approaches on synthetic data and two real-world tasks. With the synthetic dataset, we compare the performance of retrospective and incremental versions of the algorithm. In the first realworld task, we use a novel setting to test if our method and baseline methods can exhaustively catch all real-world news in the test period. The evaluation results show that when the event is quite unusual with regard to the base social media discussion, it can be captured more effectively with our method. In the second real-world task, we use the event captured to help improve the accuracy of stock market movement prediction. We show that our event-based approach has a clear advantage compared to other ways of adding social media information.