What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding

Kursuncu, Ugur; Lokala, Usha; Illendula, Anurag; Thirunarayan, Krishnaprasad; Daniulaityte, Raminta; Sheth, Amit P.; Arpinar, I. Budak

doi:10.1109/wi.2018.00-50

Cited by 13 publications

(23 citation statements)

References 27 publications

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“…As such, analyzing the high dimensional patterns within this could help enhance the user classification accuracy. In [16], Kursuncu et al leveraged three levels of features (person-level, contentlevel, and network-level) in Twitter data for representing a user, where each level of features was called a view. Compositional multi-view embedding (CME) was used for embedding the three levels of features.…”

Section: Drug Abuse Detection In Social Mediamentioning

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

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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: Drug Abuse Detection In Social Mediamentioning

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

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“…Recent research showed that semantic embeddings are more efficient than the embeddings learned using the traditional approach as they inherit semantic and syntactic knowledge, and semantic embeddings have shown great success in similarity and analogical reasoning tasks [6]. Wijeratne et al [47] have worked on learning semantic representations of emojis using knowledge concepts from EmojiNet, and these embeddings have improved the results of emoji similarity and other natural language processing tasks [27]. Recent research by Seyednezhad et al [39] and Fede et al [13] has shown that emoji cooccurrence is one of the important features which helps us to understand the context of use of multiple emojis.…”

Section: Related Workmentioning

confidence: 99%

Which Emoji Talks Best for My Picture?

Illendula

Manohar²,

Yedulla³

2018

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

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Emojis have evolved as complementary sources for expressing emotion in social-media platforms where posts are mostly composed of texts and images. In order to increase the expressiveness of the social media posts, users associate relevant emojis with their posts. Incorporating domain knowledge has improved machine understanding of text. In this paper, we investigate whether domain knowledge for emoji can improve the accuracy of emoji recommendation task in case of multimedia posts composed of image and text. Our emoji recommendation can suggest accurate emojis by exploiting both visual and textual content from social media posts as well as domain knowledge from Emojinet. Experimental results using pre-trained image classifiers and pre-trained word embedding models on Twitter dataset show that our results outperform the current state-ofthe-art by 9.6%. We also present a user study evaluation of our recommendation system on a set of images chosen from MSCOCO dataset.

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“…For example, public health agencies and healthcare providers can better target their audience for the promotion of information and services; information seekers and service users can better find credible information to fulfil their informational needs. While there exists a wealth of literature on social media user profiling in general, these are limited to either non-health context (Tinati et al , 2012; Uddin et al , 2014), or specific health-related issues such as smokers and drug users (Kim et al , 2017; Kursuncu et al , 2018). Methods and findings from these studies are ad hoc and not directly applicable to the general public health domain.…”

Section: Introductionmentioning

confidence: 99%

“…Rabarison et al (2017) identified individual and organisational users during a Twitter chat session focused on health and wellness in New Orleans. Kursuncu et al (2018) classified Twitter users in the marijuana community into retail, informed agency and personal accounts.…”

Section: Introductionmentioning

confidence: 99%

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Zhang

Bors

2019

OIR

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Purpose This work studies automated user classification on Twitter in the public health domain, a task that is essential to many public health-related research works on social media but has not been addressed. The purpose of this paper is to obtain empirical knowledge on how to optimise the classifier performance on this task. Design/methodology/approach A sample of 3,100 Twitter users who tweeted about different health conditions were manually coded into six most common stakeholders. The authors propose new, simple features extracted from the short Twitter profiles of these users, and compare a large set of classification models (including state-of-the-art) that use more complex features and with different algorithms on this data set. Findings The authors show that user classification in the public health domain is a very challenging task, as the best result the authors can obtain on this data set is only 59 per cent in terms of F1 score. Compared to state-of-the-art, the methods can obtain significantly better (10 percentage points in F1 on a “best-against-best” basis) results when using only a small set of 40 features extracted from the short Twitter user profile texts. Originality/value The work is the first to study the different types of users that engage in health-related communication on social media, applicable to a broad range of health conditions rather than specific ones studied in the previous work. The methods are implemented as open source tools, and together with data, are the first of this kind. The authors believe these will encourage future research to further improve this important task.

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What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding

Cited by 13 publications

References 27 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

Which Emoji Talks Best for My Picture?

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