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Tweets can tell: activity recognition using hybrid gated recurrent neural networks

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Abstract

This paper presents techniques to detect the “offline” activity (such as dining, shopping, or entertainment) a person is engaged in when she is tweeting , in order to create a dynamic profile of the user, for uses such as better targeting of advertisements. To this end, we present a hybrid gated recurrent neural network (GRNN)-based model for rich contextual learning. Specifically, the study and construction of the hybrid model are applied to two types of GRNNs, namely LSTM and GRU networks. In the process, we study the effects of applying and combining multiple contextual modeling methods with different contextual features. Our hybrid model outperforms a set of baselines and state-of-the-art methods. Finally, this paper presents an orthogonal validation using a real-world application. Our model generates offline activity analysis for the followers of several well-known accounts, and the result is quite representative of the expected characteristics of these accounts.

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Notes

  1. Source code is available at https://goo.gl/o9dsBh.

References

  • Atig MF, Cassel S (2014) Activity profiles in online social media. In: 2014 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 850–855

  • Bakshy E, Hofman JM (2011) Everyone’s an influencer: quantifying influence on twitter. In: Proceedings of the fourth ACM international conference on Web search and data mining. ACM, pp 65–74

  • Bansal T, Belanger D (2016) Ask the GRU: multi-task learning for deep text recommendations. In: Proceedings of the 10th ACM conference on recommender systems. ACM, pp 107–114

  • Benevenuto F, Rodrigues T (2009) Characterizing user behavior in online social networks. In: Proceedings of the 9th ACM SIGCOMM conference on internet measurement conference. ACM, pp 49–62

  • Cha M, Haddadi H (2010) Measuring user influence in twitter: the million follower fallacy. ICWSM 10(10–17):30

    Google Scholar 

  • Chollet F et al (2015) Keras. https://keras.io/

  • Cho K, Van Merriënboer B (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:14061078

  • Cui R, Agrawal G (2019) Tweets can tell: activity recognition using hybrid long short-term memory model. In: Proceedings of the 2019 IEEE/ACM international conference on advances in social networks analysis and mining, pp 164–167

  • Dhingra B, Liu H (2016) Gated-attention readers for text comprehension. arXiv preprint arXiv:160601549

  • Dhingra B, Zhou Z (2016) Tweet2vec: character-based distributed representations for social media. arXiv preprint arXiv:160503481

  • Dickinson T, Fernandez M et al (2016) Identifying important life events from twitter using semantic and syntactic patterns. In: WWW/Internet conference proceedings 2016, IADIS Press, pp 143–150

  • Ghosh S, Vinyals O (2016) Contextual LSTM (CLSTM) models for large scale NLP tasks. arXiv preprint arXiv:160206291

  • Gimpel K, Schneider N (2010) Part-of-speech tagging for twitter: annotation, features, and experiments. Technical report, Carnegie-Mellon University, Pittsburgh, PA, School of Computer Science

  • Greff K, Srivastava RK et al (2016) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28:2222–2232

    Article  MathSciNet  Google Scholar 

  • Gu X, Yang H (2018) Profiling web users using big data. Soc Netw Anal Min 8(1):24

    Article  Google Scholar 

  • Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  • Huang M, Cao Y (2016) Modeling rich contexts for sentiment classification with LSTM. arXiv preprint arXiv:160501478

  • Kapanipathi P, Jain P (2014) User interests identification on twitter using a hierarchical knowledge base. In: European semantic web conference. Springer, pp 99–113

  • Kingma DP, Ba J (2014) ADAM: a method for stochastic optimization. arXiv preprint arXiv:14126980

  • Lee WJ, Oh KJ (2014) User profile extraction from twitter for personalized news recommendation. In: 2014 16th international conference on advanced communication technology (ICACT). IEEE, pp 779–783

  • Lian D, Xie X (2011) Collaborative activity recognition via check-in history. In: Proceedings of the 3rd ACM SIGSPATIAL international workshop on location-based social networks. ACM, pp 45–48

  • Liao D, Liu W (2018) Predicting activity and location with multi-task context aware recurrent neural network. In: IJCAI, pp 3435–3441

  • Liu Y, Sun C (2016) Learning natural language inference using bidirectional LSTM model and inner-attention. arXiv preprint arXiv:160509090

  • Li J, Xu H (2016) Tweet modeling with lstm recurrent neural networks for hashtag recommendation. In: 2016 international joint conference on neural networks (IJCNN). IEEE, pp 1570–1577

  • Malmgren RD et al. (2009) Characterizing individual communication patterns. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining

  • Mehrotra R, Sanner S (2013) Improving lda topic models for microblogs via tweet pooling and automatic labeling. In: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 889–892

  • Michelson M, Macskassy SA (2010) Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the fourth workshop on analytics for noisy unstructured text data. ACM, pp 73–80

  • Mislove A, Viswanath B (2010) You are who you know: inferring user profiles in online social networks. In: Proceedings of the third ACM international conference on web search and data mining, ACM, pp 251–260

  • Noulas A, Scellato S (2011) An empirical study of geographic user activity patterns in foursquare. ICwSM 11:70–573

    Google Scholar 

  • Owoputi O, O’Connor B (2013) Improved part-of-speech tagging for online conversational text with word clusters. Association for Computational Linguistics, Stroudsburg

    Google Scholar 

  • Pennington J, Socher R (2014) GloVe: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  • Quercia D, Kosinski M (2011) Our twitter profiles, our selves: predicting personality with twitter. In: 2011 IEEE third international conference on privacy, security, risk and trust and 2011 IEEE third international conference on social computing. IEEE, pp 180–185

  • Rao D, Yarowsky D (2010) Classifying latent user attributes in twitter. In: Proceedings of the 2nd international workshop on search and mining user-generated contents. ACM, pp 37–44

  • Rozental A, Fleischer D (2018) Amobee at SemEval-2018 task 1: GRU neural network with a CNN attention mechanism for sentiment classification. arXiv preprint arXiv:180404380

  • Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45(11):2673–2681

    Article  Google Scholar 

  • Song Y, Lu Z (2013) Collaborative boosting for activity classification in microblogs. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp 482–490

  • Tuna T, Akbas E (2016) User characterization for online social networks. Soc Netw Anal Min 6(1):104

    Article  Google Scholar 

  • Vaswani A, Shazeer N (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008

  • Vosoughi S, Vijayaraghavan P (2016) Tweet2Vec: learning tweet embeddings using character-level CNN-LSTM encoder-decoder. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 1041–1044

  • Wang Y, Huang M (2016) Attention-based LSTM for aspect-level sentiment classification. In: Proceedings of the 2016 conference on empirical methods in natural language processing, pp 606–615

  • Wang X, Liu Y (2015) Predicting polarities of tweets by composing word embeddings with long short-term memory. In: Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: long papers), vol 1, pp 1343–1353

  • Weerkamp W, De Rijke M (2012) Activity prediction: a twitter-based exploration. In: SIGIR workshop on time-aware information access

  • Yang D, Zhang D (2015) Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Trans Syst Man Cybern Syst 45(1):129–142

    Article  Google Scholar 

  • Shuang-Hong Y et al (2014) Large-scale high-precision topic modeling on twitter. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

  • Yen AZ, Huang HH (2018) Detecting personal life events from twitter by multi-task lstm. In: Companion of the web conference 2018 on the web conference 2018, international world wide web conferences steering committee, pp 21–22

  • Ye J, Zhu Z (2013) What’s your next move: user activity prediction in location-based social networks. In: Proceedings of the 2013 SIAM international conference on data mining. SIAM, pp 171–179

  • Yuan S, Wu X (2018) Incorporating pre-training in long short-term memory networks for tweet classification. Soc Netw Anal Min 8(1):52

    Article  Google Scholar 

  • Zhang Z, Robinson D (2018) Detecting hate speech on twitter using a convolution-GRU based deep neural network. In: European semantic web conference. Springer, pp 745–760

  • Zhou C, Sun Cea (2015) A C-LSTM neural network for text classification. arXiv preprint arXiv:151108630

  • Zhou Q, Wen L (2016) A hierarchical lstm model for joint tasks. In: China national conference on Chinese computational linguistics. Springer, pp 324–335

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Authors and Affiliations

  1. The Ohio State University, Columbus, USA

    Renhao Cui & Rajiv Ramnath

  2. Augusta University, Augusta, USA

    Gagan Agrawal

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  1. Renhao Cui
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  2. Gagan Agrawal
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  3. Rajiv Ramnath
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Correspondence to Renhao Cui.

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Cui, R., Agrawal, G. & Ramnath, R. Tweets can tell: activity recognition using hybrid gated recurrent neural networks. Soc. Netw. Anal. Min. 10, 16 (2020). https://doi.org/10.1007/s13278-020-0628-0

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