Multiscale event detection in social media

Generalized durative event detection on social media

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 real-world 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.

A Review on the Trends in Event Detection by Analyzing Social Media Platforms' Data

Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried out to detect any disturbing event and take appropriate measures. This review paper presents a thorough survey to acquire in-depth knowledge about the current research in this field and provide a guideline for future research. We systematically review 67 articles on event detection by sensing social media data from the last decade. We summarize their event detection techniques, tools, technologies, datasets, performance metrics, etc. The reviewed papers mainly address the detection of events, such as natural disasters, traffic, sports, real-time events, and some others. As these detected events can quickly provide an overview of the overall condition of the society, they can significantly help in scrutinizing events disrupting social security. We found that compatibility with different languages, spelling, and dialects is one of the vital challenges the event detection algorithms face. On the other hand, the event detection algorithms need to be robust to process different media, such as texts, images, videos, and locations. We outline that the event detection techniques compatible with heterogeneous data, language, and the platform are still missing. Moreover, the event and its location with a 24 × 7 real-time detection system will bolster the overall event detection performance.

Burst: real-time events burst detection in social text stream

Gigantic growth of social media and unbeatable trend of progress in the direction of the web seeking user's interests have generated a storm of social text streams. Seeking information to know the phenomenon of various events in the early stages is quite interesting. Various kinds of social media live streams attract users to participate in real-time events to become a part of an immense crowd. However, the vast amount of text is present on social media, the unnecessary information bogs a social text stream filtering to extract the appropriate topics and events effectively. Therefore, detecting, classifying, and identifying burst events is quite challenging due to the sparse and noisy text of Twitter. The researchers' significant open challenges are the effective cleaning and profound representation of the text stream data. This research article's main contribution is to provide a detailed study and explore bursty event detection in the social text stream. Thus, this work's main motive is to present a concise approach that classifies and detects the event keywords and maintains the record of the event based on related features. These features permit the approach to successfully determine the booming pattern of events scrupulously at different time span. Experiments are conducted and compared with the state-of-the-art methods, which reveals that the proposed approach is proficient to detect valuable patterns of interest and also achieve better scoresto extract burst events on social media posted by various users.

Event Detection System Based on User Behavior Changes in Online Social Networks: Case of the COVID-19 Pandemic

People use Online Social Networks (OSNs) to express their opinions and feelings about many topics. Depending on the nature of an event and its dissemination rate in OSNs, and considering specific regions, the users' behavior can drastically change over a specific period of time. In this context, this work aims to propose an event detection system at the early stages of an event based on changes in the users' behavior in an OSN. This system can detect an event of any subject, and thus, it can be used for different purposes. The proposed event detection system is composed of the following main modules: (1) determination of the user's location, (2) message extraction from an OSN, (3) topic identification using natural language processing (NLP) based on the Deep Belief Network (DBN), (4) the user behavior change analyzer in the OSN, and (5) affective analysis for emotion identification based on a tree-convolutional neural network (tree-CNN). In the case of public health, the early event detection is very relevant for the population and the authorities in order to be able take corrective actions. Hence, the new coronavirus disease (COVID-19) is used as a case study in this work. For performance validation, the modules related to the topic identification and affective analysis were compared with other similar solutions or implemented with other machine learning algorithms. In the performance assessment, the proposed event detection system achieved an accuracy higher than 0.90, while other similar methods reached accuracy values less than 0.74. Additionally, our proposed system was able to detect an event almost three days earlier than the other methods. Furthermore, the information provided by the system permits to understand the predominant characteristics of an event, such as keywords and emotion type of messages.

Tracking Events in Twitter by Combining an LDA-Based Approach and a Density–Contour Clustering Approach

Nowadays, Twitter has become one of the fastest-growing microblogging services; consequently, analyzing this rich and continuously user-generated content can reveal unprecedentedly valuable knowledge. In this paper, we propose a novel two-stage system to detect and track events from tweets by integrating a Latent Dirichlet Allocation (LDA)-based approach and an efficient density–contour-based spatio-temporal clustering approach. In the proposed system, we first divide the geotagged tweet stream into temporal time windows; next, events are identified as topics in tweets using an LDA-based topic discovery step; then, each tweet is assigned an event label; next, a density–contour-based spatio-temporal clustering approach is employed to identify spatio-temporal event clusters. In our approach, topic continuity is established by calculating KL-divergences between topics and spatio-temporal continuity is established by a family of newly formulated spatial cluster distance functions. Moreover, the proposed density–contour clustering approach considers two types of densities: “absolute” density and “relative” density to identify event clusters where either there is a high density of event tweets or there is a high percentage of event tweets. We evaluate our approach using real-world data collected from Twitter, and the experimental results show that the proposed system can not only detect and track events effectively but also discover interesting patterns from geotagged tweets.

A bibliometric analysis of event detection in social media

Purpose

The purpose of this paper is to explore the research status and development trend of the field of event detection in social media (ED in SM) through a bibliometric analysis of academic publications.

Design/methodology/approach

First, publication distributions are analyzed including the trends of publications and citations, subject distribution, predominant journals, affiliations, authors, etc. Second, an indicator of collaboration degree is used to measure scientific connective relations from different perspectives. A network analysis method is then applied to reveal scientific collaboration relations. Furthermore, based on keyword co-occurrence analysis, major research themes and their evolutions throughout time span are discovered. Finally, a network analysis method is applied to visualize the analysis results.

Findings

The area of ED in SM has received increasing attention and interest in academia with Computer Science and Engineering as two major research subjects. The USA and China contribute the most to the area development. Affiliations and authors tend to collaborate more with those within the same country. Among the 14 identified research themes, newly emerged themes such as Pharmacovigilance event detection are discovered.

Originality/value

This study is the first to comprehensively illustrate the research status of ED in SM by conducting a bibliometric analysis. Up-to-date findings are reported, which can help relevant researchers understand the research trend, seek scientific collaborators and optimize research topic choices.