Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks

Saeed, Zafar; Abbasi, Rabeeh Ayaz; Razzak, Muhammad Imran; Maqbool, Onaiza; Sadaf, Abida; Xu, Guandong

doi:10.1016/j.eswa.2019.06.005

Cited by 31 publications

(28 citation statements)

References 31 publications

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“…Tracking dynamics of words in terms of graph, or converting sentences into graph representation and trying to understand the spikes inside, is a very useful method. e graph heartbeat model, introduced by [31], and its enhanced version [32] are all based on this fact. ey used graph analytics to detect the emerging events from Twitter data stream by using graph based formulation and spike detection.…”

Section: Frequent Pattern Mining Methods Frequent Pattern Mining Methods Have Been Applied To Tdt Task In Twittermentioning

confidence: 99%

Topic Detection and Tracking Techniques on Twitter: A Systematic Review

et al. 2021

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Social networks are real-time platforms formed by users involving conversations and interactions. This phenomenon of the new information era results in a very huge amount of data in different forms and modalities such as text, images, videos, and voice. The data with such characteristics are also known as big data with 5-V properties and in some cases are also referred to as social big data. To find useful information from such valuable data, many researchers tried to address different aspects of it for different modalities. In the case of text, NLP researchers conducted many research studies and scientific works to extract valuable information such as topics. Many enlightening works on different platforms of social media, like Twitter, tried to address the problem of finding important topics from different aspects and utilized it to propose solutions for diverse use cases. The importance of Twitter in this scope lies in its content and the behavior of its users. For example, it is also known as first-hand news reporting social media which has been a news reporting and informing platform even for political influencers or catastrophic news reporting. In this review article, we cover more than 50 research articles in the scope of topic detection from Twitter. We also address deep learning-based methods.

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Section: Frequent Pattern Mining Methods Frequent Pattern Mining Methods Have Been Applied To Tdt Task In Twittermentioning

confidence: 99%

Topic Detection and Tracking Techniques on Twitter: A Systematic Review

et al. 2021

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“…Another example is the Conditional Random Field (CRF). There are drawbacks to applying these cutting edge NLP methodologies and graph based techniques [33], [34] to biomedical text mining though. Given that word representation models like Word2Vec [22], ELMo [28], BERT [6] and ALBERT [12] are trained primarily on datasets which have general non-specific domain texts (i.e, Wikipedia), it becomes hard to gauge their performance on biomedical text-containing datasets.…”

Section: Introductionmentioning

confidence: 99%

Biomedical Named-Entity Recognition by Hierarchically Fusing BioBERT Representations and Deep Contextual-Level Word-Embedding

Naseem

Musiał

Eklund

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Text mining in the biomedical domain is increasingly important as the volume of biomedical documents increases. Thanks to advances in natural language processing (NLP), extracting valuable information from the biomedical literature is gaining popularity among researchers, and deep learning has enabled the development of effective biomedical text mining models. However, directly applying advancements in NLP to biomedical sources often yields unsatisfactory results, due to a word distribution drift from the general language domain corpora to specific biomedical corpora, and this drift introduces linguistic ambiguities. To overcome these challenges, this paper presents a novel method for biomedical named entity-recognition (BioNER) through hierarchically fusing representations from BioBERT, which is trained on biomedical corpora and Deep contextual-level word embeddings to handle the linguistic challenges within biomedical literature. Proposed text representation is then fed to attention-based Bi-directional Long Short Term Memory (BiLSTM) with Conditional random field (CRF) for the BioNER task. The experimental analysis shows that our proposed end-to-end methodology outperforms existing state-ofthe-art methods for the BioNER task.

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“…It is naive to employ burstiness as a key feature to detect the occurrence of events. A rise in tweets frequency related to a long term event often dominates other small but newsworthy events [7,8]. People report major events more often for an extended period.…”

Section: Introductionmentioning

confidence: 99%

“…Event detection approach, and performance comparison with existing approaches are available in[7][8][9].…”

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confidence: 99%

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EveSense: What Can You Sense from Twitter?

Saeed

Abbasi

Razzak

2020

Lecture Notes in Computer Science

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Social media has become a useful source for detecting real-life events. This paper presents an event detection application EveSense. It detects real-life events and related trending topics from the Twitter stream and allows users to find interesting events that have recently occurred. It uses a novel Dynamic Heartbeat Graph (DHG) approach, which efficiently extracts distinguishing features and performs better than the existing event detection methods. We tested and evaluated the application on three case studies, including a sports event (FA cup Final) and two political events (Super Tuesday and US Election).

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Enhanced Heartbeat Graph for emerging event detection on Twitter using time series networks

Cited by 31 publications

References 31 publications

Topic Detection and Tracking Techniques on Twitter: A Systematic Review

Topic Detection and Tracking Techniques on Twitter: A Systematic Review

Biomedical Named-Entity Recognition by Hierarchically Fusing BioBERT Representations and Deep Contextual-Level Word-Embedding

EveSense: What Can You Sense from Twitter?

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