Topic extraction in social media

Rafea, Ahmed; Mostafa, Nada A.

doi:10.1109/cts.2013.6567212

Cited by 13 publications

(10 citation statements)

References 20 publications

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“…AHC was used to detect events from Tweets by (Parikh and Karlapalem, 2013). Rafea and Mostafa (2013) applied bisecting k-mean algorithm to identify Arabic hot events from Twitter. Hu et al (2017) proposed novel document representation method based on word embed-dings to reduce dimensionality feature space and applied new adaptive single pass clustering method for online news event detection.…”

Section: Clustering-based Methodsmentioning

confidence: 99%

“…Different from Twitter, some SNs (e.g., Facebook) have privacy issues that limit the collection process to just offline publicly available data that have been given permissions to be collected (Chen et al, 2016). Becker et al (2011a;Phuvipadawat and Murata, 2010;Sakaki et al, 2010;Popescu et al, 2011;Ritter et al, 2012;Li et al, 2012a;Weng and Lee, 2011;Sankaranarayanan et al, 2009;Culotta, 2010;Osborne et al, 2012;Subašić and Berendt, 2011;Becker et al, 2012;Abhik and Toshniwal, 2013;Petrović et al, 2010;Ishikawa et al, 2012;Mathioudakis and Koudas, 2010;Long et al, 2011;Rosa et al, 2011;Popescu and Pennacchiotti, 2010;Benson et al, 2011;Lee and Sumiya, 2010;Mehrotra et al, 2013;Rajani et al, 2014;Weng et al, 2010;Zhao et al, 2011;Diao et al, 2012;Dong et al, 2015;Fang et al, 2014;Cataldi et al, 2010;Cordeiro, 2012;Kwan et al, 2013;Weiler et al, 2014;Aggarwal and Subbian, 2012;Tembhurnikar and Patil, 2015;Katragadda et al, 2017;Manaskasemsak et al, 2016;Zhang et al, 2015;Huang et al, 2013;Kaleel et al, 2013;Rafea an...…”

Section: Concepts Of Event Detectionmentioning

confidence: 99%

See 1 more Smart Citation

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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Event Detection (ED) is a study area that attracts the attention of decision-makers from various disciplines in order to help them in taking the right decision. ED has been examined on various text streams like Twitter, Facebook, Emails, Blogs, Web Forums and newswires. Many ED models have been proposed in literature. In general, ED model consists of six main phases: Data collection, pre-processing, feature selection, event detection, performance evaluation and result representation. Among these phases, event detection phase has a vital rule in the performance of the ED model. Consequently, numerous supervised, unsupervised, semi-supervised detection methods have been introduced for this phase. However, unsupervised methods have been extensively utilized as ED process is considered as unsupervised task. Hence, such methods need to be categorized on such a way so it can help researchers to understand and identified the limitations lay in these methods. 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. In addition, main categories for unsupervised detection methods are explicitly mentioned with revising their related works. Moreover, the major open challenges faced by researchers for building ED models are explained and discussed in detail. The main objective of this survey paper is to provide a complete view of the recent developments in ED field. Hence, help scholars to identify the limitations of existing ED models for text data and help them to recognize the interesting future works directions.

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Section: Clustering-based Methodsmentioning

confidence: 99%

Section: Concepts Of Event Detectionmentioning

confidence: 99%

A Survey on Event Detection Models for Text Data Streams

Al-Dyani¹,

Ahmad²,

Kamaruddin³

2020

Journal of Computer Science

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“…Social media contains a high volume of expressions by individuals and groups alike. However, most of these expressions can be aggregated into a finite number of themes and topics [72]. We conducted a topic analysis and a topic association analysis to explore the distinct and common themes across the four stages of the pandemic.…”

Section: Topics and Topic Associationsmentioning

confidence: 99%

Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence

Adikari¹,

Nawaratne²,

Silva³

et al. 2021

J Med Internet Res

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Background:The COVID-19 pandemic has disrupted human societies around the world. This public health emergency was followed by a significant loss of human life; the ensuing social restrictions led to loss of employment, lack of interactions, and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups, and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental well-being of all individuals impacted by the pandemic.Objective: This research aims to investigate the human emotions related to the COVID-19 pandemic expressed on social media over time, using an artificial intelligence (AI) framework.Methods: Our study explores emotion classifications, intensities, transitions, and profiles, as well as alignment to key themes and topics, across the four stages of the pandemic: declaration of a global health crisis (ie, prepandemic), the first lockdown, easing of restrictions, and the second lockdown. This study employs an AI framework comprised of natural language processing, word embeddings, Markov models, and the growing self-organizing map algorithm, which are collectively used to investigate social media conversations. The investigation was carried out using 73,000 public Twitter conversations posted by users in Australia from January to September 2020. Results:The outcomes of this study enabled us to analyze and visualize different emotions and related concerns that were expressed and reflected on social media during the COVID-19 pandemic, which could be used to gain insights into citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people had expressed during the four stages of the pandemic. It was noted that personal-level concerns expressed on social media had escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that fear and sadness emotions were more prominently expressed at first; however, emotions transitioned into anger and disgust over time. Negative emotions, except for sadness, were significantly higher (P<.05) in the second lockdown, showing increased frustration. Temporal emotion analysis was conducted by modeling the emotion state changes across the four stages of the pandemic, which demonstrated how different emotions emerged and shifted over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles.Conclusions: This study showed that the diverse emotions and concerns that were expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study established the use of social media to discover informed insights during a time when physical communication was impossible, the outcomes could also contribute toward postpandemic...

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“…Sharma et al [7] gathered vast Twitter messages for eight months, made by 79,768 Twitter clients, and channelled them through Natural Language Processing (NLP) to create different categories. Rafea and Mostafa [8] investigated Arabic text with k-means. The frequent words (words that occurred more than 20 times) in the entire corpus were utilized as elements to arrange the tweets via the k-mean classifier.…”

Section: Related Workmentioning

confidence: 99%