Public Reaction to Scientific Research via Twitter Sentiment Prediction

Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.-H., & Liu, B.Y. (2011). Predicting flu trends using twitter data. In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp. 702–707. IEEE. AchrekarH. GandheA. LazarusR. YuS.-H. LiuB.Y. 2011 Predicting flu trends using twitter data In 2011 IEEE conference on computer communications workshops (INFOCOM WKSHPS) 702 707 IEEE 10.1109/INFCOMW.2011.5928903 Search in Google Scholar

Akella, A.P., Alhoori, H., Kondamudi, P.R., Freeman, C., & Zhou, H. (2021). Early indicators of scientific impact: Predicting citations with altmetrics. Journal of Informetrics, 15(2), 101128. AkellaA.P. AlhooriH. KondamudiP.R. FreemanC. ZhouH. 2021 Early indicators of scientific impact: Predicting citations with altmetrics Journal of Informetrics 15 2 101128 10.1016/j.joi.2020.101128 Search in Google Scholar

Alhoori, H. (2016). How to identify specialized research communities related to a researcher's changing interests. In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL ’16, pp. 239–240, New York, NY, USA. Association for Computing Machinery. AlhooriH. 2016 How to identify specialized research communities related to a researcher's changing interests In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries, JCDL ’16 239 240 New York, NY, USA Association for Computing Machinery 10.1145/2910896.2925450 Search in Google Scholar

Alhoori, H., & Furuta, R. (2014). Do altmetrics follow the crowd or does the crowd follow altmetrics? In IEEE/ACM Joint Conference on Digital Libraries, pp. 375–378. AlhooriH. FurutaR. 2014 Do altmetrics follow the crowd or does the crowd follow altmetrics? In IEEE/ACM Joint Conference on Digital Libraries 375 378 10.1109/JCDL.2014.6970193 Search in Google Scholar

Alhoori, H., & Furuta, R. (2017). Recommendation of scholarly venues based on dynamic user interests. Journal of Informetrics, 11(2), 553–563. AlhooriH. FurutaR. 2017 Recommendation of scholarly venues based on dynamic user interests Journal of Informetrics 11 2 553 563 10.1016/j.joi.2017.03.006 Search in Google Scholar

Alhoori, H., Furuta, R., Tabet, M., Samaka, M., & Fox, E.A. (2014). Altmetrics for country-level research assessment. In International Conference on Asian Digital Libraries, pp. 59–64. Springer. AlhooriH. FurutaR. TabetM. SamakaM. FoxE.A. 2014 Altmetrics for country-level research assessment In International Conference on Asian Digital Libraries 59 64 Springer 10.1007/978-3-319-12823-8_7 Search in Google Scholar

Alhoori, H., Ray Choudhury, S., Kanan, T., Fox, E., Furuta, R., & Giles, C.L. (2015). On the relationship between open access and altmetrics. iConference 2015 Proceedings. AlhooriH. Ray ChoudhuryS. KananT. FoxE. FurutaR. GilesC.L. 2015 On the relationship between open access and altmetrics iConference 2015 Proceedings Search in Google Scholar

Alhoori, H., Samaka, M., Furuta, R., & Fox, E.A. (2019). Anatomy of scholarly information behavior patterns in the wake of academic social media platforms. International Journal on Digital Libraries, 20(4), 369–389. AlhooriH. SamakaM. FurutaR. FoxE.A. 2019 Anatomy of scholarly information behavior patterns in the wake of academic social media platforms International Journal on Digital Libraries 20 4 369 389 10.1007/s00799-018-0255-9 Search in Google Scholar

Amolik, A., Jivane, N., Bhandari, M., & Venkatesan, M. (2016). Twitter sentiment analysis of movie reviews using machine learning techniques. International Journal of Engineering and Technology, 7(6), 1–7. AmolikA. JivaneN. BhandariM. VenkatesanM. 2016 Twitter sentiment analysis of movie reviews using machine learning techniques International Journal of Engineering and Technology 7 6 1 7 Search in Google Scholar

Bae, Y., & Lee, H. (2012). Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers. Journal of the American Society for Information Science and technology, 63(12), 2521–2535. BaeY. LeeH. 2012 Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers Journal of the American Society for Information Science and technology 63 12 2521 2535 10.1002/asi.22768 Search in Google Scholar

Bermingham, A., & Smeaton, A. (2011). On using twitter to monitor political sentiment and predict election results. In Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011), pp. 2–10. BerminghamA. SmeatonA. 2011 On using twitter to monitor political sentiment and predict election results In Proceedings of the Workshop on Sentiment Analysis where AI meets Psychology (SAAIP 2011) 2 10 Search in Google Scholar

Bharathwaj, S.K., Na, J.-C., Sangeetha, B., & Sarathkumar, E. (2019). Sentiment analysis of tweets mentioning research articles in medicine and psychiatry disciplines. In International Conference on Asian Digital Libraries, pp. 303–307. Springer. BharathwajS.K. NaJ.-C. SangeethaB. SarathkumarE. 2019 Sentiment analysis of tweets mentioning research articles in medicine and psychiatry disciplines In International Conference on Asian Digital Libraries 303 307 Springer 10.1007/978-3-030-34058-2_29 Search in Google Scholar

Chatterjee, A., Gupta, U., Chinnakotla, M.K., Srikanth, R., Galley, M., & Agrawal, P. (2019). Understanding emotions in text using deep learning and big data. Computers in Human Behavior, 93, 309–317. ChatterjeeA. GuptaU. ChinnakotlaM.K. SrikanthR. GalleyM. AgrawalP. 2019 Understanding emotions in text using deep learning and big data Computers in Human Behavior 93 309 317 10.1016/j.chb.2018.12.029 Search in Google Scholar

Chawla, N.V., Bowyer, K.W., Hall, L.O., & Kegelmeyer, W.P. (2002). Smote: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. ChawlaN.V. BowyerK.W. HallL.O. KegelmeyerW.P. 2002 Smote: Synthetic minority over-sampling technique Journal of Artificial Intelligence Research 16 321 357 10.1613/jair.953 Search in Google Scholar

Chen, X.Y., Cho, Y.W., & Jang, S.Y. (2015). Crime prediction using twitter sentiment and weather. In 2015 Systems and Information Engineering Design Symposium, pp. 63–68. IEEE. ChenX.Y. ChoY.W. JangS.Y. 2015 Crime prediction using twitter sentiment and weather In 2015 Systems and Information Engineering Design Symposium 63 68 IEEE 10.1109/SIEDS.2015.7117012 Search in Google Scholar

Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. CohenJ. 1960 A coefficient of agreement for nominal scales Educational and Psychological Measurement 20 1 37 46 10.1177/001316446002000104 Search in Google Scholar

Da Silva, N.F., Hruschka, E.R., & Hruschka Jr, E.R. (2014). Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66, 170–179. Da SilvaN.F. HruschkaE.R. HruschkaE.R.Jr 2014 Tweet sentiment analysis with classifier ensembles Decision Support Systems 66 170 179 10.1016/j.dss.2014.07.003 Search in Google Scholar

Didegah, F., Mejlgaard, N., & Sørensen, M.P. (2018). Investigating the quality of interactions and public engagement around scientific papers on twitter. Journal of Informetrics, 12(3), 960–971. DidegahF. MejlgaardN. SørensenM.P. 2018 Investigating the quality of interactions and public engagement around scientific papers on twitter Journal of Informetrics 12 3 960 971 10.1016/j.joi.2018.08.002 Search in Google Scholar

Freeman, C., Alhoori, H., & Shahzad, M. (2020). Measuring the diversity of face-book reactions to research. Proceedings of the ACM on Human-Computer Interaction, 4(GROUP), 1–17. FreemanC. AlhooriH. ShahzadM. 2020 Measuring the diversity of face-book reactions to research Proceedings of the ACM on Human-Computer Interaction, 4(GROUP) 1 17 10.1145/3375192 Search in Google Scholar

Freeman, C., Roy, M.K., Fattoruso, M., & Alhoori, H. (2019). Shared feelings: Understanding facebook reactions to scholarly articles. In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 301–304. IEEE. FreemanC. RoyM.K. FattorusoM. AlhooriH. 2019 Shared feelings: Understanding facebook reactions to scholarly articles In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL) 301 304 IEEE 10.1109/JCDL.2019.00050 Search in Google Scholar

Friedrich, N., Bowman, T.D., & Haustein, S. (2015). Do tweets to scientific articles contain positive or negative sentiments. In Altmetrics Workshop, Amsterdam. Retrieved from http://altmetrics.org/altmetrics15/friedrich. FriedrichN. BowmanT.D. HausteinS. 2015 Do tweets to scientific articles contain positive or negative sentiments In Altmetrics Workshop Amsterdam Retrieved from http://altmetrics.org/altmetrics15/friedrich. Search in Google Scholar

Friedrich, N., Bowman, T.D., Stock, W.G., & Haustein, S. (2015). Adapting sentiment analysis for tweets linking to scientific papers. arXiv preprint arXiv:1507.01967. FriedrichN. BowmanT.D. StockW.G. HausteinS. 2015 Adapting sentiment analysis for tweets linking to scientific papers arXiv preprint arXiv:1507.01967. Search in Google Scholar

Gayo-Avello, D. (2012). No, you cannot predict elections with twitter. IEEE Internet Computing, 16(6), 91–94. Gayo-AvelloD. 2012 No, you cannot predict elections with twitter IEEE Internet Computing 16 6 91 94 10.1109/MIC.2012.137 Search in Google Scholar

Hansson, K., & Ludwig, T. (2019). Crowd dynamics: Conflicts, contradictions, and community in crowdsourcing. Computer Supported Cooperative Work (CSCW), 28(5), 791–794. HanssonK. LudwigT. 2019 Crowd dynamics: Conflicts, contradictions, and community in crowdsourcing Computer Supported Cooperative Work (CSCW) 28 5 791 794 10.1007/s10606-018-9343-z Search in Google Scholar

Hansson, K., Ludwig, T., & Aitamurto, T. (2019). Capitalizing relationships: Modes of participation in crowdsourcing. Computer Supported Cooperative Work (CSCW), 28(5), 977–1000. HanssonK. LudwigT. AitamurtoT. 2019 Capitalizing relationships: Modes of participation in crowdsourcing Computer Supported Cooperative Work (CSCW) 28 5 977 1000 10.1007/s10606-018-9341-1 Search in Google Scholar

Hao, M., Rohrdantz, C., Janetzko, H., Dayal, U., Keim, D.A., Haug, L.-E., & Hsu, M.-C. (2011). Visual sentiment analysis on twitter data streams. In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 277–278. IEEE. HaoM. RohrdantzC. JanetzkoH. DayalU. KeimD.A. HaugL.-E. HsuM.-C. 2011 Visual sentiment analysis on twitter data streams In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST) 277 278 IEEE 10.1109/VAST.2011.6102472 Search in Google Scholar

Hassan, A., Abbasi, A., & Zeng, D. (2013). Twitter sentiment analysis: A bootstrap ensemble framework. In 2013 international conference on social computing, pp. 357–364. IEEE. HassanA. AbbasiA. ZengD. 2013 Twitter sentiment analysis: A bootstrap ensemble framework In 2013 international conference on social computing 357 364 IEEE 10.1109/SocialCom.2013.56 Search in Google Scholar

Hassan, S.-U., Saleem, A., Soroya, S.H., Safder, I., Iqbal, S., Jamil, S., Bukhari, F., Aljohani, N.R., & Nawaz, R. (2020). Sentiment analysis of tweets through altmetrics: A machine learning approach. Journal of Information Science, 0165551520930917. HassanS.-U. SaleemA. SoroyaS.H. SafderI. IqbalS. JamilS. BukhariF. AljohaniN.R. NawazR. 2020 Sentiment analysis of tweets through altmetrics: A machine learning approach Journal of Information Science 0165551520930917. 10.1177/0165551520930917 Search in Google Scholar

Haunschild, R., Leydesdorff, L., & Bornmann, L. (2020). Library and information science papers discussed on twitter: A new network-based approach for measuring public attention. Journal of Data and Information Science, 5(3), 5–17. HaunschildR. LeydesdorffL. BornmannL. 2020 Library and information science papers discussed on twitter: A new network-based approach for measuring public attention Journal of Data and Information Science 5 3 5 17 10.2478/jdis-2020-0017 Search in Google Scholar

Haustein, S. (2019). Scholarly twitter metrics. In Springer handbook of science and technology indicators, pp. 729–760. Springer. HausteinS. 2019 Scholarly twitter metrics In Springer handbook of science and technology indicators 729 760 Springer 10.1007/978-3-030-02511-3_28 Search in Google Scholar

Hussein, D.M.E.-D.M. (2018). A survey on sentiment analysis challenges. Journal of King Saud University-Engineering Sciences, 30(4), 330–338. HusseinD.M.E.-D.M. 2018 A survey on sentiment analysis challenges Journal of King Saud University-Engineering Sciences 30 4 330 338 10.1016/j.jksues.2016.04.002 Search in Google Scholar

Ibrahim, N.F., & Wang, X. (2019). Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media. Computers in Human Behavior, 96, 32–45. IbrahimN.F. WangX. 2019 Decoding the sentiment dynamics of online retailing customers: Time series analysis of social media Computers in Human Behavior 96 32 45 10.1016/j.chb.2019.02.004 Search in Google Scholar

Jaidka, K., Guntuku, S.C., Lee, J.H., Luo, Z., Buffone, A., & Ungar, L.H. (2021). The rural–urban stress divide: Obtaining geographical insights through twitter. Computers in Human Behavior, 114, 106544. JaidkaK. GuntukuS.C. LeeJ.H. LuoZ. BuffoneA. UngarL.H. 2021 The rural–urban stress divide: Obtaining geographical insights through twitter Computers in Human Behavior 114 106544 10.1016/j.chb.2020.106544 Search in Google Scholar

Kale, B., Siravuri, H.V., Alhoori, H., & Papka, M.E. (2017). Predicting research that will be cited in policy documents. In Proceedings of the 2017 ACM on Web Science Conference, WebSci ’17, pp. 389–390, New York, NY, USA. Association for Computing Machinery. KaleB. SiravuriH.V. AlhooriH. PapkaM.E. 2017 Predicting research that will be cited in policy documents In Proceedings of the 2017 ACM on Web Science Conference, WebSci ’17 389 390 New York, NY, USA Association for Computing Machinery 10.1145/3091478.3098865 Search in Google Scholar

Kharde, V., & Sonawane, S. (2016). Sentiment analysis of twitter data: A survey of techniques. International Journal of Computer Applications, 139(11), 5–15. KhardeV. SonawaneS. 2016 Sentiment analysis of twitter data: A survey of techniques International Journal of Computer Applications 139 11 5 15 10.5120/ijca2016908625 Search in Google Scholar

Kou, Y., Kow, Y.M., Gui, X., & Cheng, W. (2017). One social movement, two social media sites: A comparative study of public discourses. Computer Supported Cooperative Work (CSCW), 26(4), 807–836. KouY. KowY.M. GuiX. ChengW. 2017 One social movement, two social media sites: A comparative study of public discourses Computer Supported Cooperative Work (CSCW) 26 4 807 836 10.1007/s10606-017-9284-y Search in Google Scholar

Kouloumpis, E., Wilson, T., & Moore, J. (2011). Twitter sentiment analysis: The good the bad and the omg! In Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 538–541. KouloumpisE. WilsonT. MooreJ. 2011 Twitter sentiment analysis: The good the bad and the omg! In Proceedings of the International AAAI Conference on Web and Social Media 5 1 538 541 10.1609/icwsm.v5i1.14185 Search in Google Scholar

Kousha, K., & Thelwall, M. (2019). An automatic method to identify citations to journals in news stories: A case study of uk newspapers citing web of science journals. Journal of Data and Information Science, 4(3), 73–95. KoushaK. ThelwallM. 2019 An automatic method to identify citations to journals in news stories: A case study of uk newspapers citing web of science journals Journal of Data and Information Science 4 3 73 95 10.2478/jdis-2019-0016 Search in Google Scholar

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Morgan & Claypool Publishers. LiuB. 2012 Sentiment analysis and opinion mining Synthesis Morgan & Claypool Publishers 10.2200/S00416ED1V01Y201204HLT016 Search in Google Scholar

Mandel, B., Culotta, A., Boulahanis, J., Stark, D., Lewis, B., & Rodrigue, J. (2012). A demographic analysis of online sentiment during hurricane irene. In Proceedings of the second workshop on language in social media, pp. 27–36. MandelB. CulottaA. BoulahanisJ. StarkD. LewisB. RodrigueJ. 2012 A demographic analysis of online sentiment during hurricane irene In Proceedings of the second workshop on language in social media 27 36 Search in Google Scholar

McKinney, W. (2011). Pandas: A foundational python library for data analysis and statistics. Python for High Performance and Scientific Computing, 14(9), 1–9. McKinneyW. 2011 Pandas: A foundational python library for data analysis and statistics Python for High Performance and Scientific Computing 14 9 1 9 Search in Google Scholar

Mittal, A., & Goel, A. (2012). Stock prediction using twitter sentiment analysis. Standford University, CS229. Retrieved from http://cs229.stanford.edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.pdf. MittalA. GoelA. 2012 Stock prediction using twitter sentiment analysis Standford University CS229. Retrieved from http://cs229.stanford.edu/proj2011/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.pdf. Search in Google Scholar

Mohammad, S.M. (2017). Challenges in sentiment analysis. In A practical guide to sentiment analysis, pp. 61–83. Springer. MohammadS.M. 2017 Challenges in sentiment analysis In A practical guide to sentiment analysis 61 83 Springer 10.1007/978-3-319-55394-8_4 Search in Google Scholar

Narr, S., Hulfenhaus, M., & Albayrak, S. (2012). Language-independent twitter sentiment analysis. Knowledge discovery and machine learning (KDML), LWA, pp. 12–14. NarrS. HulfenhausM. AlbayrakS. 2012 Language-independent twitter sentiment analysis Knowledge discovery and machine learning (KDML), LWA 12 14 Search in Google Scholar

Neethu, M., & Rajasree, R. (2013). Sentiment analysis in twitter using machine learning techniques. In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–5. IEEE. NeethuM. RajasreeR. 2013 Sentiment analysis in twitter using machine learning techniques In 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT) 1 5 IEEE 10.1109/ICCCNT.2013.6726818 Search in Google Scholar

Noyons, E. (2019). Measuring societal impact is as complex as ABC. Journal of Data and Information Science, 4(3), 6–21. NoyonsE. 2019 Measuring societal impact is as complex as ABC Journal of Data and Information Science 4 3 6 21 10.2478/jdis-2019-0012 Search in Google Scholar

Pak, A., & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of European Language Resource Association, volume 10, pp. 1320–1326. PakA. ParoubekP. 2010 Twitter as a corpus for sentiment analysis and opinion mining In Proceedings of European Language Resource Association 10 1320 1326 Search in Google Scholar

Pal, J., Thawani, U., Van Der Vlugt, E., Out, W., Chandra, P., et al. (2018). Speaking their mind: Populist style and antagonistic messaging in the tweets of donald trump, narendra modi, nigel farage, and geert wilders. Computer Supported Cooperative Work (CSCW), 27(3), 293–326. PalJ. ThawaniU. Van Der VlugtE. OutW. ChandraP. 2018 Speaking their mind: Populist style and antagonistic messaging in the tweets of donald trump, narendra modi, nigel farage, and geert wilders Computer Supported Cooperative Work (CSCW) 27 3 293 326 10.1007/s10606-018-9316-2 Search in Google Scholar

Pandian, N.D.S., Na, J.-C., Veeramachaneni, B., & Boothaladinni, R.V. (2019). Altmetrics: Factor analysis for assessing the popularity of research articles on twitter. Journal of Information Science Theory and Practice, 7(4), 33–44. PandianN.D.S. NaJ.-C. VeeramachaneniB. BoothaladinniR.V. 2019 Altmetrics: Factor analysis for assessing the popularity of research articles on twitter Journal of Information Science Theory and Practice 7 4 33 44 Search in Google Scholar

Parikh, R., & Movassate, M. (2009). Sentiment analysis of user-generated twitter updates using various classification techniques. CS224N Final Report, 118. ParikhR. MovassateM. 2009 Sentiment analysis of user-generated twitter updates using various classification techniques CS224N Final Report, 118. Search in Google Scholar

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12, 2825–2830. PedregosaF. VaroquauxG. GramfortA. MichelV. ThirionB. GriselO. 2011 Scikit-learn: Machine learning in python The Journal of machine Learning research 12 2825 2830 Search in Google Scholar

Pozzi, F.A., Fersini, E., Messina, E., & Liu, B. (2017). Challenges of sentiment analysis in social networks: An overview. Sentiment analysis in social networks, pp. 1–11. PozziF.A. FersiniE. MessinaE. LiuB. 2017 Challenges of sentiment analysis in social networks: An overview Sentiment analysis in social networks 1 11 10.1016/B978-0-12-804412-4.00001-2 Search in Google Scholar

Raamkumar, A.S., Ganesan, S., Jothiramalingam, K., Selva, M.K., Erdt, M., & Theng, Y.-L. (2018). Investigating the characteristics and research impact of sentiments in tweets with links to computer science research papers. In International Conference on Asian Digital Libraries, pp. 71–82. Springer. RaamkumarA.S. GanesanS. JothiramalingamK. SelvaM.K. ErdtM. ThengY.-L. 2018 Investigating the characteristics and research impact of sentiments in tweets with links to computer science research papers In International Conference on Asian Digital Libraries 71 82 Springer 10.1007/978-3-030-04257-8_7 Search in Google Scholar

Saif, H., Fernandez, M., He, Y., & Alani, H. (2014). On stopwords, filtering and data sparsity for sentiment analysis of Twitter. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pp. 810–817, Reykjavik, Iceland. European Language Resources Association (ELRA). SaifH. FernandezM. HeY. AlaniH. 2014 On stopwords, filtering and data sparsity for sentiment analysis of Twitter In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14) 810 817 Reykjavik, Iceland European Language Resources Association (ELRA) Search in Google Scholar

Shaban, H. (2019). Twitter reveals its daily active user numbers for the first time. Washington Post. Retrieved from https://www.latimes.com/business/technology/la-fi-tn-twitter-daily-users-20190207-story.html ShabanH. 2019 Twitter reveals its daily active user numbers for the first time Washington Post Retrieved from https://www.latimes.com/business/technology/la-fi-tn-twitter-daily-users-20190207-story.html Search in Google Scholar

Shaikh, A.R., & Alhoori, H. (2019). Predicting patent citations to measure economic impact of scholarly research. In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL), pp. 400–401. ShaikhA.R. AlhooriH. 2019 Predicting patent citations to measure economic impact of scholarly research In 2019 ACM/IEEE Joint Conference on Digital Libraries (JCDL) 400 401 10.1109/JCDL.2019.00089 Search in Google Scholar

Siravuri, H.V., Akella, A.P., Bailey, C., & Alhoori, H. (2018). Using social media and scholarly text to predict public understanding of science. In Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, JCDL ’18, pp. 385–386, New York, NY, USA. Association for Computing Machinery. SiravuriH.V. AkellaA.P. BaileyC. AlhooriH. 2018 Using social media and scholarly text to predict public understanding of science In Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, JCDL ’18 385 386 New York, NY, USA Association for Computing Machinery 10.1145/3197026.3203890 Search in Google Scholar

Siravuri, H.V., & Alhoori, H. (2017). What makes a research article newsworthy? Proceedings of the Association for Information Science and Technology, 54(1), 802–803. SiravuriH.V. AlhooriH. 2017 What makes a research article newsworthy? Proceedings of the Association for Information Science and Technology 54 1 802 803 10.1002/pra2.2017.14505401163 Search in Google Scholar

Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61, 2544–2558. ThelwallM. BuckleyK. PaltoglouG. CaiD. KappasA. 2010 Sentiment strength detection in short informal text Journal of the American Society for Information Science and Technology 61 2544 2558 10.1002/asi.21416 Search in Google Scholar

Thelwall, M., Tsou, A., Holmberg, K., & Haustein, S. (2013). Tweeting links to academic articles. Cybermetrics, 17(1), 1–8. ThelwallM. TsouA. HolmbergK. HausteinS. 2013 Tweeting links to academic articles Cybermetrics 17 1 1 8 Search in Google Scholar

Vinkers, C.H., Tijdink, J.K., & Otte, W.M. (2015). Use of positive and negative words in scientific pubmed abstracts between 1974 and 2014: retrospective analysis. BMJ, 351. VinkersC.H. TijdinkJ.K. OtteW.M. 2015 Use of positive and negative words in scientific pubmed abstracts between 1974 and 2014: retrospective analysis BMJ 351 10.1136/bmj.h6467467769526668206 Search in Google Scholar

Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system for real-time twitter sentiment analysis of 2012 us presidential election cycle. In Proceedings of the ACL 2012 system demonstrations, pp. 115–120. WangH. CanD. KazemzadehA. BarF. NarayananS. 2012 A system for real-time twitter sentiment analysis of 2012 us presidential election cycle In Proceedings of the ACL 2012 system demonstrations 115 120 Search in Google Scholar

Wang, X., Gerber, M.S., & Brown, D.E. (2012). Automatic crime prediction using events extracted from twitter posts. In International conference on social computing, behavioral-cultural modeling, and prediction, pp. 231–238. Springer. WangX. GerberM.S. BrownD.E. 2012 Automatic crime prediction using events extracted from twitter posts In International conference on social computing, behavioral-cultural modeling, and prediction 231 238 Springer 10.1007/978-3-642-29047-3_28 Search in Google Scholar

Wang, X.L., Wei, F.R., Liu, X.H., Zhou, M., & Zhang, M. (2011). Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach. In Proceedings of the 20th ACM international conference on Information and knowledge management, pp. 1031–1040. WangX.L. WeiF.R. LiuX.H. ZhouM. ZhangM. 2011 Topic sentiment analysis in twitter: A graph-based hashtag sentiment classification approach In Proceedings of the 20th ACM international conference on Information and knowledge management 1031 1040 10.1145/2063576.2063726 Search in Google Scholar

Le, X.Q., Chu, J.D., Deng, S.Y., Jiao, Q.H., Pei, J.J., et al. (2019). Citeopinion: Evidence-based evaluation tool for academic contributions of research papers based on citing sentences. Journal of Data and Information Science, 4(4), 26–41. LeX.Q. ChuJ.D. DengS.Y. JiaoQ.H. PeiJ.J. 2019 Citeopinion: Evidence-based evaluation tool for academic contributions of research papers based on citing sentences Journal of Data and Information Science 4 4 26 41 10.2478/jdis-2019-0019 Search in Google Scholar

Zaman, T.R., Herbrich, R., Van Gael, J., & Stern, D. (2010). Predicting information spreading in twitter. In Workshop on computational social science and the wisdom of crowds, nips, volume 104, pp. 17599–601. Citeseer. ZamanT.R. HerbrichR. Van GaelJ. SternD. 2010 Predicting information spreading in twitter In Workshop on computational social science and the wisdom of crowds, nips 104 17599 601 Citeseer Search in Google Scholar