Birds of prey: identifying lexical irregularities in spam on Twitter

Robinson, Kyle; Mago, Vijay

doi:10.1007/s11276-018-01900-9

Cited by 11 publications

(6 citation statements)

References 9 publications

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“…Additionally, due to the exponential growth of social media data, real-time data processing is essential in practice [ 40 ]. Providing solutions to the challenges such as dynamic updates in the training dataset and the filtration of spam tweets [ 7 ] is the next step.…”

Section: Discussionmentioning

confidence: 99%

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Utilizing deep learning and graph mining to identify drug use on Twitter data

Tassone

Yan

Simpson

et al. 2020

BMC Med Inform Decis Mak

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Background The collection and examination of social media has become a useful mechanism for studying the mental activity and behavior tendencies of users. Through the analysis of a collected set of Twitter data, a model will be developed for predicting positively referenced, drug-related tweets. From this, trends and correlations can be determined. Methods Social media data (tweets and attributes) were collected and processed using topic pertaining keywords, such as drug slang and use-conditions (methods of drug consumption). Potential candidates were preprocessed resulting in a dataset of 3,696,150 rows. The predictive classification power of multiple methods was compared including SVM, XGBoost, BERT and CNN-based classifiers. For the latter, a deep learning approach was implemented to screen and analyze the semantic meaning of the tweets. Results To test the predictive capability of the model, SVM and XGBoost were first employed. The results calculated from the models respectively displayed an accuracy of 59.33% and 54.90%, with AUC’s of 0.87 and 0.71. The values show a low predictive capability with little discrimination. Conversely, the CNN-based classifiers presented a significant improvement, between the two models tested. The first was trained with 2661 manually labeled samples, while the other included synthetically generated tweets culminating in 12,142 samples. The accuracy scores were 76.35% and 82.31%, with an AUC of 0.90 and 0.91. Using association rule mining in conjunction with the CNN-based classifier showed a high likelihood for keywords such as “smoke”, “cocaine”, and “marijuana” triggering a drug-positive classification. Conclusion Predictive analysis with a CNN is promising, whereas attribute-based models presented little predictive capability and were not suitable for analyzing text of data. This research found that the commonly mentioned drugs had a level of correspondence with frequently used illicit substances, proving the practical usefulness of this system. Lastly, the synthetically generated set provided increased accuracy scores and improves the predictive capability.

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Section: Discussionmentioning

confidence: 99%

“…Twitter data is not the most consistent or stable information to work with[ 6 , 7 ]. Inconsistencies within the wording and the lack of discrete variables made analysis and classification a difficult task.…”

Section: Introductionmentioning

confidence: 99%

Utilizing deep learning and graph mining to identify drug use on Twitter data

Tassone

Yan

Simpson

et al. 2020

BMC Med Inform Decis Mak

Self Cite

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“…We initially retrieved over 5000 hashtagged tweets related to the two events by using Twitter’s Advanced Search facility. The selection process (Robinson and Mago, 2018) resulted in eliminating stop-words, emojis, duplications, spam and tweets that were not related to the events at all (including ads, undecipherable text). After selecting the top-performing messages ( n = 40) and those which safely matched the circumstances at stake, we lined them up alongside narratives retrieved from news stories ( n = 40) about the same events and dates.…”

Section: Methodsmentioning

confidence: 99%

Exploring narrative linearity between Twitter and the news: Echoes of the Arab Spring in Brazil

Levy

Mercea

2021

Discourse & Society

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This article explores the use of narrative theory as an analytical framework to investigate the extent to which popular hashtags and the news can develop into intersecting stories. It juxtaposes the case of hashtag-based reports seen during the Arab Spring to understand the coverage of notorious political episodes in Brazil. Namely, the 2016 impeachment of Dilma Rousseff and the 2018 election of Jair Bolsonaro. Here, narrative linearity emerges as a tool to observe the borrowing of Twitter hashtags in several journalistic pieces. It is contended that the linearity of authorship, narration and representation of time appears as a satisfactory pathway to trace the development of hashtags into popular news stories. Results suggested that hashtags can significantly follow narratives and agendas in journalism but differing from their original social media context.

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“…Distinctive opinions and polarity of words used by the writer in an essay shape up the overall essay construction and quality, specifically in persuasive and argumentative essays [24]. Many NLP tasks have used sentiment analysis such as in social media [25], movie's reviews [26], news and politics [27]. One of the first attempts at incorporating sentiments in AES involved using subjective lexicon(s) to get the polarity of the sentences [28].…”

Section: B Sentiment Analysis In Essay Evaluationmentioning

confidence: 99%

Syntactic, Semantic and Sentiment Analysis: The Joint Effect on Automated Essay Evaluation

Janda

Pawar

et al. 2019

IEEE Access

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Manual grading of essays by humans is time-consuming and likely to be susceptible to inconsistencies and inaccuracies. In recent years, an abundance of research has been done to automate essay evaluation processes, yet little has been done to take into consideration the syntax, semantic coherence and sentiments of the essay's text together. Our proposed system incorporates not just the rule-based grammar and surface level coherence check but also includes the semantic similarity of the sentences. We propose to use Graph-based relationships within the essay's content and polarity of opinion expressions. Semantic similarity is determined between each statement of the essay to form these Graph-based spatial relationships and novel features are obtained from it. Our algorithm uses 23 salient features with high predictive power, which is less than the current systems while considering every aspect to cover the dimensions that a human grader focuses on. Fewer features help us get rid of the redundancies of the data so that the predictions are based on more representative features and are robust to noisy data. The prediction of the scores is done with neural networks using the data released by the ASAP competition held by Kaggle. The resulting agreement between human grader's score and the system's prediction is measured using Quadratic Weighted Kappa (QWK). Our system produces a QWK of 0.793. INDEX TERMS Natural language processing, semantic analysis, sentiment analysis and text mining, automated essay evaluation.

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Birds of prey: identifying lexical irregularities in spam on Twitter

Cited by 11 publications

References 9 publications

Utilizing deep learning and graph mining to identify drug use on Twitter data

Utilizing deep learning and graph mining to identify drug use on Twitter data

Exploring narrative linearity between Twitter and the news: Echoes of the Arab Spring in Brazil

Syntactic, Semantic and Sentiment Analysis: The Joint Effect on Automated Essay Evaluation

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