Abstract
Information extraction (IE) is a common sub-area of natural language processing that focuses on identifying structured data from unstructured data. The community of Information Retrieval (IR) relies on accurate and high-performance IE to be able to retrieve high quality results from massive datasets. One example of IE is to identify named entities in a text, e.g., “Barack Obama served as the president of the USA”. Here, Barack Obama and USA are named entities of types of PERSON and LOCATION, respectively. Another example is to identify sentiment expressed in a text, e.g., “This movie was awesome”. Here, the sentiment expressed is positive. Finally, identifying various linguistic aspects of a text, e.g., part of speech tags, noun phrases, dependency parses, etc., which can serve as features for additional IE tasks. This tutorial introduces participants to a) the usage of Python based, open-source tools that support IE from social media data (mainly Twitter), and b) best practices for ensuring the reproducibility of research. Participants will learn and practice various semantic and syntactic IE techniques that are commonly used for analyzing tweets. Additionally, participants will be familiarized with the landscape of publicly available tweet data, and methods for collecting and preparing them for analysis. Finally, participants will be trained to use a suite of open source tools (SAIL for active learning, TwitterNER for named entity recognition3, and SocialMediaIE for multi task learning), which utilize advanced machine learning techniques (e.g., deep learning, active learning with human-in-the-loop, multi-lingual, and multi-task learning) to perform IE on their own or existing datasets. Participants will also learn how social context can be integrated in Information Extraction systems to make them better. The tools introduced in the tutorial will focus on the three main stages of IE, namely, collection of data (including annotation), data processing and analytics, and visualization of the extracted information. More details can be found at: https://socialmediaie.github.io/tutorials/.
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References
Addawood, A., Rezapour, R., Mishra, S., Schneider, J., Diesner, J.: Developing an information source lexicon. In: Prioritising Online Content Workshop Co-located at NIPS (2017)
Collier, D., Mishra, S., Houston, D., Hensley, B., Mitchell, S., Hartlep, N.: Who is most likely to oppose federal tuition-free college policies? Investigating variable interactions of sentiments to America’s college promise. SSRN Electron. J. (2019). https://doi.org/10.2139/ssrn.3423054
Collier, D.A., Mishra, S., Houston, D.A., Hensley, B.O., Hartlep, N.D.: Americans ‘support’ the idea of tuition-free college: an exploration of sentiment and political identity signals otherwise. J. Furth. High. Educ. 43(3), 347–362 (2019). https://doi.org/10.1080/0309877X.2017.1361516
Diesner, J., Carley, K.M.: Relation extraction from texts (in German: Extraktion relationaler Daten aus Texten). In: Stegbauer, C., Häußling, R. (eds.) Handbook network research (Handbuch Netzwerkforschung), pp. 507–521. VS Verlag (2010)
Diesner, J., Kumaraguru, P., Carley, K.M.: Mental models of data privacy and security extracted from interviews with Indians. In: Proceedings of 55th Annual Conference of International Communication Association (ICA). New York, NY (2005)
Diesner, J., Chin, C.L.: Usable ethics: practical considerations for responsibly conducting research with social trace data. In: Proceedings of Beyond IRBs: Ethical Review Processes for Big Data Research (2015)
Diesner, J., Chin, C.L.: Seeing the forest for the trees: considering applicable types of regulation for the responsible collection and analysis of human centered data. In: Human-Centered Data Science (HCDS) Workshop at 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (2016)
Eisenstein, J.: What to do about bad language on the internet. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 359–369. Association for Computational Linguistics, Atlanta, Georgia (June 2013)
Han, K., Yang, P., Mishra, S., Diesner, J.: WikiCSSH: extracting computer science subject headings from Wikipedia. In: Workshop on Scientific Knowledge Graphs (SKG 2020) (2020)
Hutto, C.J., Gilbert, E.: Vader: A parsimonious rule-based model for sentiment analysis of social media text. In: International AAAI Conference on Web and Social Media. Ann Arbor, Michigan, USA (2014)
Kaplan, A.M., Haenlein, M.: Users of the world, unite! The challenges and opportunities of social media. Bus. Horiz. 53(1), 59–68 (2010). https://doi.org/10.1016/j.bushor.2009.09.003
Kosinski, M., Matz, S.C., Gosling, S.D., Popov, V., Stillwell, D.: Facebook as a research tool for the social sciences: opportunities, challenges, ethical considerations, and practical guidelines. Am. Psychol. 70(6), 543–556 (2015). https://doi.org/10.1037/a0039210
Kulkarni, V., Mishra, S., Haghighi, A.: LMSOC: an approach for socially sensitive pretraining. In: Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 2967–2975. Association for Computational Linguistics, Stroudsburg, PA, USA (November 2021). https://doi.org/10.18653/v1/2021.findings-emnlp.254
Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th international conference on World wide web–WWW 2010, p. 591. ACM Press, New York, New York, USA (April 2010). https://doi.org/10.1145/1772690.1772751
Mishra, S.: SCTG: social communications temporal graph - a novel approach to visualize temporal communication graphs from social data. In: UIUC Data Science Day (October 2017)
Mishra, S.: Multi-dataset-multi-task neural sequence tagging for information extraction from tweets. In: Proceedings of the 30th ACM Conference on Hypertext and Social Media - HT 2019, pp. 283–284. ACM Press, New York, New York, USA (2019). https://doi.org/10.1145/3342220.3344929
Mishra, S.: Information extraction from digital social trace data with applications to social media and scholarly communication data. ACM SIGIR Forum 54(1), 1–2 (2020). https://doi.org/10.1145/3451964.3451981
Mishra, S.: Information Extraction from Digital Social Trace Data with Applications to Social Media and Scholarly Communication Data. Ph.D. thesis, University of Illinois at Urbana-Champaign (2020)
Mishra, S.: Non-neural structured prediction for event detection from news in Indian languages. In: Mehta, P., Mandl, T., Majumder, P., Mitra, M. (eds.) Working Notes of FIRE 2020–Forum for Information Retrieval Evaluation. CEUR Workshop Proceedings, CEUR-WS.org, Hyderabad, India (2020)
Mishra, S., Agarwal, S., Guo, J., Phelps, K., Picco, J., Diesner, J.: Enthusiasm and support: alternative sentiment classification for social movements on social media. In: Proceedings of the 2014 ACM conference on Web science - WebSci 2014, pp. 261–262. ACM Press, Bloomington, Indiana, USA (June 2014). https://doi.org/10.1145/2615569.2615667
Mishra, S., Collier, D.: A framework for generating annotated social media corpora with demographics, stance, civility, and topicality. SSRN Electron. J. (2020). https://doi.org/10.2139/ssrn.3757554
Mishra, S., Diesner, J.: Semi-supervised named entity recognition in noisy-text. In: Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pp. 203–212. The COLING 2016 Organizing Committee, Osaka, Japan (2016)
Mishra, S., Diesner, J.: Detecting the correlation between sentiment and user-level as well as text-level meta-data from benchmark corpora. In: Proceedings of the 29th on Hypertext and Social Media - HT 2018, pp. 2–10. ACM Press, New York, New York, USA (2018). https://doi.org/10.1145/3209542.3209562
Mishra, S., Diesner, J.: Capturing signals of enthusiasm and support towards social issues from Twitter. In: Proceedings of the 5th International Workshop on Social Media World Sensors - SIdEWayS 2019, pp. 19–24. ACM Press, New York, New York, USA (2019). https://doi.org/10.1145/3345645.3351104
Mishra, S., Diesner, J., Byrne, J., Surbeck, E.: Sentiment analysis with incremental human-in-the-loop learning and lexical resource customization. In: Proceedings of the 26th ACM Conference on Hypertext & Social Media - HT 2015, pp. 323–325. ACM Press, New York, New York, USA (2015). https://doi.org/10.1145/2700171.2791022
Mishra, S., Haghighi, A.: Improved multilingual language model pretraining for social media text via translation pair prediction. In: Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pp. 381–388. Association for Computational Linguistics, Stroudsburg, PA, USA (November 2021). https://doi.org/10.18653/v1/2021.wnut-1.42
Mishra, S., He, S., Belli, L.: Assessing demographic bias in named entity recognition. In: Bias in Automatic Knowledge Graph Construction–A Workshop at AKBC 2020 (August 2020)
Mishra, S., Mishra, S.: 3Idiots at HASOC 2019: fine-tuning transformer neural networks for hate speech identification in Indo-European languages. In: Proceedings of the 11th Annual Meeting of the Forum for Information Retrieval Evaluation, pp. 208–213. Kolkata, India (2019)
Mishra, S., Mishra, S.: Scubed at 3C task a–a simple baseline for citation context purpose classification. In: Proceedings of the 8th International Workshop on Mining Scientific Publications, pp. 59–64. Association for Computational Linguistics, Wuhan, China (2020)
Mishra, S., Mishra, S.: Scubed at 3C task b–a simple baseline for citation context influence classification. In: Proceedings of the 8th International Workshop on Mining Scientific Publications, pp. 65–70. Association for Computational Linguistics, Wuhan, China (2020)
Mishra, S., Prasad, S., Mishra, S.: Multilingual joint fine-tuning of transformer models for identifying trolling, aggression and cyberbullying at TRAC 2020. In: Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying, pp. 120–125. European Language Resources Association (ELRA), Marseille, France (2020)
Mishra, S., Prasad, S., Mishra, S.: Exploring multi-task multi-lingual learning of transformer models for hate speech and offensive speech identification in social media. SN Comput. Sci. 2(2), 1–19 (2021). https://doi.org/10.1007/s42979-021-00455-5
Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), vol. 2, pp. 321–327. Association for Computational Linguistics, Atlanta, Georgia, USA (2013)
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retr. 2(1–2), 1–135 (2008). https://doi.org/10.1561/1500000011
Rezapour, R., Dinh, L., Diesner, J.: Incorporating the measurement of moral foundations theory into analyzing stances on controversial topics. In: Proceedings of the 32st ACM Conference on Hypertext and Social Media, pp. 177–188. ACM, New York, NY, USA (August 2021). https://doi.org/10.1145/3465336.3475112
Rezapour, R., Shah, S.H., Diesner, J.: Enhancing the measurement of social effects by capturing morality. In: Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 35–45. Association for Computational Linguistics, Stroudsburg, PA, USA (2019). https://doi.org/10.18653/v1/W19-1305
Rezapour, R., Wang, L., Abdar, O., Diesner, J.: Identifying the overlap between election result and candidates’ ranking based on hashtag-enhanced, lexicon-based sentiment analysis. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC), pp. 93–96. IEEE (2017). https://doi.org/10.1109/ICSC.2017.92
Sarawagi, S.: Information extraction. Found. Trends® Databases 1(3), 261–377 (2007). https://doi.org/10.1561/1900000003
Sarol, M.J., Dinh, L., Rezapour, R., Chin, C.L., Yang, P., Diesner, J.: An empirical methodology for detecting and prioritizing needs during crisis events. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 4102–4107. Association for Computational Linguistics, Stroudsburg, PA, USA (2020). https://doi.org/10.18653/v1/2020.findings-emnlp.366
Schwartz, H.A., et al.: Personality, gender, and age in the language of social media: the open-vocabulary approach. PLoS ONE 8(9), e73791 (2013). https://doi.org/10.1371/journal.pone.0073791
Yee, K., Tantipongpipat, U., Mishra, S.: Image cropping on twitter: fairness metrics, their limitations, and the importance of representation, design, and agency. Proc. ACM Hum. Comput. Interact. 5(CSCW2), 1–24 (2021). https://doi.org/10.1145/3479594
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Mishra, S., Rezapour, R., Diesner, J. (2022). Information Extraction from Social Media: A Hands-On Tutorial on Tasks, Data, and Open Source Tools. In: Hagen, M., et al. Advances in Information Retrieval. ECIR 2022. Lecture Notes in Computer Science, vol 13186. Springer, Cham. https://doi.org/10.1007/978-3-030-99739-7_74
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