A rule dynamics approach to event detection in Twitter with its application to sports and politics

Adedoyin-Olowe, Mariam; Gaber, Mohamed Medhat; Dancausa, Carlos M.; Stahl, Frederic; Gomes, João Bártolo

doi:10.1016/j.eswa.2016.02.028

Cited by 70 publications

(48 citation statements)

References 20 publications

(25 reference statements)

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“…Figure 4 shows the ROC curves for the results of a single fold of Naive Bayes classification that uses the features extracted by each selection methods. The classification results of the proposed method outperformed the benchmarks and state of the art developed by [40], [8], [39], [10], [1], [25].…”

Section: Experiments and Resultsmentioning

confidence: 86%

“…This method uses the concept of distributional semantic, where the co-occurring signals are likely to be semantically associated [22]. Example 1: Original Tweet: Protesters may be unmasked in wake of Coburg clash https://t.co/djjVIfzO3e (News) #melbourne #victoria Assuming a time frame of 20 days word-pair: [2,3,3,4,5,3,2,3,8,3,3,1,3,9,3,1,2,4,5,1] Spikes (B OW ): [0,0,0,0,1,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0] Events(GT ): [0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,1,0]…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Event Detection in Twitter: A Keyword Volume Approach

Hossny

Mitchell

2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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Event detection using social media streams needs a set of informative features with strong signals that need minimal preprocessing and are highly associated with events of interest. Identifying these informative features as keywords from Twitter is challenging, as people use informal language to express their thoughts and feelings. This informality includes acronyms, misspelled words, synonyms, transliteration and ambiguous terms. In this paper, we propose an efficient method to select the keywords frequently used in Twitter that are mostly associated with events of interest such as protests. The volume of these keywords is tracked in real time to identify the events of interest in a binary classification scheme. We use keywords within wordpairs to capture the context. The proposed method is to binarize vectors of daily counts for each word-pair by applying a spike detection temporal filter, then use the Jaccard metric to measure the similarity of the binary vector for each word-pair with the binary vector describing event occurrence. The top n wordpairs are used as features to classify any day to be an event or non-event day. The selected features are tested using multiple classifiers such as Naive Bayes, SVM, Logistic Regression, KNN and decision trees. They all produced AUC ROC scores up to 0.91 and F1 scores up to 0.79. The experiment is performed using the English language in multiple cities such as Melbourne, Sydney and Brisbane as well as the Indonesian language in Jakarta. The two experiments, comprising different languages and locations, yielded similar results.

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Section: Experiments and Resultsmentioning

confidence: 86%

Section: Experiments and Resultsmentioning

confidence: 99%

Event Detection in Twitter: A Keyword Volume Approach

Hossny

Mitchell

2018

2018 IEEE International Conference on Data Mining Workshops (ICDMW)

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“…We also combine aggregated centrality and growth factor by multiplying both features to calculate heartbeat score (12).…”

Section: Aggregated Centrality and Growth Factor Basedmentioning

confidence: 99%

“…The experiments are conducted on three well-known benchmarks: FA Cup, Super Tuesday, and the US Election datasets [11]. Many recent studies [12], [13], [14], [2], [15], [16], [17] use these benchmarks to evaluate the performance of their approaches. The details of the three datasets are given in Table 1.…”

Section: Datasetmentioning

confidence: 99%

Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]

Saeed

Abbasi

Razzak

et al. 2019

IEEE Comput. Intell. Mag.

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Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream. Once an event is detected in a Twitter stream, WDHG suppresses it in later stages, in order to detect new emerging events. This unique characteristic makes the proposed approach sensitive to capture emerging events efficiently. Experiments are performed on three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and the US Elections 2012. Results show considerable improvement over existing event detection methods in most cases.

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“…Some recent studies have analysed hashtags. For example, Adedoyin-Olowe, Gaber, Dancausa, Stahl, and Gomes (2016) used hashtags and association rules to explore valuable news. Hamed, Wu, Erickson, and Fandy (2015) examined hashtags and association rules to find the correlation among drugs.…”

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confidence: 99%

Developing a personal value analysis method of social media to support customer segmentation and business model innovation

2019

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Companies need to find out about the personal values of customers and identify customer segments before developing effective business models (BMs) or marketing strategies. Therefore, understanding the personal values of customers is critical in BM design. Traditional personal value evaluation methods are laborious and time‐consuming. In recent years, social media have become an important platform for people to share ideas and express views, making the presence of a huge amount of opinions on platforms that can cater for the demand of data for personal value analysis of customers. This study took Facebook and Instagram as the targets and developed a novel personal value forecasting method to help enterprises obtain the various personal values of customer segments automatically at a lower cost. This study adopted Schwartz's value theory as the value model and proposed a consistency model and a relativity model for weighted calculations, so as to determine the feature of a value tag. Finally, the feature selection algorithm and classification algorithm were used for judging values. In the evaluation phase of this study, 61 participants were recruited to test the proposed method. The proposed method could assist enterprises in better understanding personal value information.

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A rule dynamics approach to event detection in Twitter with its application to sports and politics

Cited by 70 publications

References 20 publications

Event Detection in Twitter: A Keyword Volume Approach

Event Detection in Twitter: A Keyword Volume Approach

Event Detection in Twitter Stream Using Weighted Dynamic Heartbeat Graph Approach [Application Notes]

Developing a personal value analysis method of social media to support customer segmentation and business model innovation

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