{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T11:08:23Z","timestamp":1774523303227,"version":"3.50.1"},"reference-count":92,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T00:00:00Z","timestamp":1568332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["DTA"],"published-print":{"date-parts":[[2019,10,22]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Hate speech in recent times has become a troubling development. It has different meanings to different people in different cultures. The anonymity and ubiquity of the social media provides a breeding ground for hate speech and makes combating it seems like a lost battle. However, what may constitute a hate speech in a cultural or religious neutral society may not be perceived as such in a polarized multi-cultural and multi-religious society like Nigeria. Defining hate speech, therefore, may be contextual. Hate speech in Nigeria may be perceived along ethnic, religious and political boundaries. The purpose of this paper is to check for the presence of hate speech in social media platforms like Twitter, and to what degree is hate speech permissible, if available? It also intends to find out what monitoring mechanisms the social media platforms like Facebook and Twitter have put in place to combat hate speech. Lexalytics is a term coined by the authors from the words lexical analytics for the purpose of opinion mining unstructured texts like tweets.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This research developed a Python software called polarized opinions sentiment analyzer (POSA), adopting an ego social network analytics technique in which an individual\u2019s behavior is mined and described. POSA uses a customized<jats:italic>Python N-Gram<\/jats:italic>dictionary of local context-based terms that may be considered as hate terms. It then applied the Twitter API to stream tweets from popular and trending Nigerian Twitter handles in politics, ethnicity, religion, social activism, racism, etc., and filtered the tweets against the custom dictionary using unsupervised classification of the texts as either positive or negative sentiments. The outcome is visualized using tables, pie charts and word clouds. A similar implementation was also carried out using R-Studio codes and both results are compared and a<jats:italic>t<\/jats:italic>-test was applied to determine if there was a significant difference in the results. The research methodology can be classified as both qualitative and quantitative. Qualitative in terms of data classification, and quantitative in terms of being able to identify the results as either negative or positive from the computation of text to vector.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The findings from two sets of experiments on POSA and R are as follows: in the first experiment, the POSA software found that the Twitter handles analyzed contained between 33 and 55 percent hate contents, while the R results show hate contents ranging from 38 to 62 percent. Performing a<jats:italic>t<\/jats:italic>-test on both positive and negative scores for both POSA and R-studio, results reveal<jats:italic>p<\/jats:italic>-values of 0.389 and 0.289, respectively, on an<jats:italic>\u03b1<\/jats:italic>value of 0.05, implying that there is no significant difference in the results from POSA and R. During the second experiment performed on 11 local handles with 1,207 tweets, the authors deduce as follows: that the percentage of hate contents classified by POSA is 40 percent, while the percentage of hate contents classified by R is 51 percent. That the accuracy of hate speech classification predicted by POSA is 87 percent, while free speech is 86 percent. And the accuracy of hate speech classification predicted by R is 65 percent, while free speech is 74 percent. This study reveals that neither Twitter nor Facebook has an automated monitoring system for hate speech, and no benchmark is set to decide the level of hate contents allowed in a text. The monitoring is rather done by humans whose assessment is usually subjective and sometimes inconsistent.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>This study establishes the fact that hate speech is on the increase on social media. It also shows that hate mongers can actually be pinned down, with the contents of their messages. The POSA system can be used as a plug-in by Twitter to detect and stop hate speech on its platform. The study was limited to public Twitter handles only. N-grams are effective features for word-sense disambiguation, but when using N-grams, the feature vector could take on enormous proportions and in turn increasing sparsity of the feature vectors.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>The findings of this study show that if urgent measures are not taken to combat hate speech there could be dare consequences, especially in highly polarized societies that are always heated up along religious and ethnic sentiments. On daily basis tempers are flaring in the social media over comments made by participants. This study has also demonstrated that it is possible to implement a technology that can track and terminate hate speech in a micro-blog like Twitter. This can also be extended to other social media platforms.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Social implications<\/jats:title><jats:p>This study will help to promote a more positive society, ensuring the social media is positively utilized to the benefit of mankind.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The findings can be used by social media companies to monitor user behaviors, and pin hate crimes to specific persons. Governments and law enforcement bodies can also use the POSA application to track down hate peddlers.<\/jats:p><\/jats:sec>","DOI":"10.1108\/dta-01-2019-0007","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T09:48:45Z","timestamp":1568368125000},"page":"501-527","source":"Crossref","is-referenced-by-count":15,"title":["Combating the challenges of social media hate speech in a polarized society"],"prefix":"10.1108","volume":"53","author":[{"given":"Collins","family":"Udanor","sequence":"first","affiliation":[]},{"given":"Chinatu C.","family":"Anyanwu","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2019,9,13]]},"reference":[{"key":"key2021041510010531900_ref001","first-page":"595","article-title":"Community member retrieval on social media using textual information","year":"2018"},{"key":"key2021041510010531900_ref002","first-page":"294","article-title":"A focused crawler for mining hate and extremism promoting videos on YouTube","year":"2014"},{"key":"key2021041510010531900_ref003","doi-asserted-by":"publisher","article-title":"Network analysis tools","year":"2015","DOI":"10.1109\/CSNT.2014.83"},{"issue":"15","key":"key2021041510010531900_ref004","first-page":"166","article-title":"Audience perception of hate speech and foul language in the social media in Nigeria: implications for morality and law","volume":"VIII","year":"2017","journal-title":"Academicus International Scientific Journal"},{"key":"key2021041510010531900_ref005","unstructured":"Albert, A.Y. (2018), \u201cNotes on machine learning and A.I. generating N-grams from sentences python\u201d, available at: www.albertauyeung.com\/post\/generating-ngrams-python\/ (accessed June 3, 2018)."},{"key":"key2021041510010531900_ref006","unstructured":"American Library Association (2017), \u201cHate speech and hate crime\u201d, available at: www.ala.org\/advocacy\/intfreedom\/hate (accessed April 2, 2019)."},{"issue":"1","key":"key2021041510010531900_ref007","doi-asserted-by":"crossref","first-page":"11","DOI":"10.32614\/RJ-2011-003","article-title":"Content-based social network analysis of mailing lists","volume":"3","year":"2011","journal-title":"The R Journal"},{"key":"key2021041510010531900_ref008","first-page":"1","article-title":"A survey on hate speech detection using natural language processing","year":"2017"},{"key":"key2021041510010531900_ref009","unstructured":"Aondover, E.M. (2018), \u201cCurbing hate speeches on social media: in letters\u201d, available at: http:\/\/thenationonlineng.net\/curbing-hate-speeches-social-media\/ (accessed March 31, 2018)."},{"key":"key2021041510010531900_ref010","unstructured":"Archer, J. (2018), \u201cThe telegraph technology intelligence\u201d, Twitter hires academics to monitor its \u201chealth\u201d and combat hate speech, available at: www.telegraph.co.uk\/technology\/2018\/07\/30\/twitter-hires-academics-monitor-healthand-combat-hate-speech\/ (accessed April 28, 2019)."},{"key":"key2021041510010531900_ref012","first-page":"759","article-title":"Deep learning for hate speech detection in tweets","year":"2017"},{"key":"key2021041510010531900_ref013","unstructured":"Barbara, O. (2018), \u201cFacebook says it\u2019s getting better at removing hate speech\u201d, available at: https:\/\/phys.org\/news\/2018-11-facebook-speech.html (accessed April 19, 2019)."},{"key":"key2021041510010531900_ref014","unstructured":"Barthel, M., Shearer, E., Gottfried, J. and Mitchell, A. (2016), \u201cThe evolving role of news on Twitter and Facebook\u201d, Pew Research Center\u2019s Journalism Project, available at: www.pewresearch.org\/wp-content\/uploads\/sites\/8\/2015\/07\/Twitter-and-News-Survey-Report-FINAL2.pdf (accessed February 4, 2016)."},{"issue":"1","key":"key2021041510010531900_ref015","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s00146-014-0549-4","article-title":"Social media analytics: a survey of techniques, tools, and platforms","volume":"30","year":"2015","journal-title":"AI & Society"},{"key":"key2021041510010531900_ref016","doi-asserted-by":"crossref","unstructured":"Benesch, S. (2014), \u201cCountering dangerous speech: new ideas for genocide prevention\u201d, working paper, Dangerous Speech Project, United States Holocaust Memorial Museum, Washington, DC, available at: https:\/\/dangerousspeech.org\/ (accessed May 15, 2018).","DOI":"10.2139\/ssrn.3686876"},{"issue":"3","key":"key2021041510010531900_ref077","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s10618-012-0259-9","article-title":"Using EmotiBlog to annotate and analyze subjectivity in the new textual genres","volume":"25","year":"2012","journal-title":"Data Mining Knowledge Discovery"},{"issue":"5","key":"key2021041510010531900_ref018","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1108\/IntR-03-2017-0100","article-title":"Tolerating and managing extreme speech on social media","volume":"28","year":"2018","journal-title":"Internet Research"},{"issue":"2","key":"key2021041510010531900_ref019","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1002\/poi3.85","article-title":"Cyber hate speech on twitter: an application of machine classification and statistical modeling for policy and decision making","volume":"7","year":"2015","journal-title":"Policy & Internet"},{"key":"key2021041510010531900_ref020","first-page":"1","article-title":"Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack","volume":"4","year":"2014","journal-title":"Social Network Analysis and Mining"},{"issue":"1","key":"key2021041510010531900_ref021","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1111\/j.1467-839X.2007.00241.x","article-title":"Social network analysis: a methodological introduction","volume":"11","year":"2008","journal-title":"Asian Journal of Psychology"},{"issue":"2","key":"key2021041510010531900_ref022","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1109\/MIS.2013.30","article-title":"New avenues in opinion mining and sentiment analysis","volume":"28","year":"2013","journal-title":"IEEE Intelligent Systems"},{"key":"key2021041510010531900_ref080","article-title":"A framework for locating and analyzing hate groups in blogs","year":"2006"},{"issue":"1","key":"key2021041510010531900_ref023","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/MWC.2015.7054715","article-title":"AIWAC: affective interaction through wearable computing and cloud technology","volume":"22","year":"2015","journal-title":"IEEE Wireless Communications"},{"key":"key2021041510010531900_ref024","unstructured":"Cherian, G. (2018), \u201cHATE SPEECH: a dilemma for journalists the world over\u201d, available at: https:\/\/ethicaljournalismnetwork.org\/resources\/publications\/ethics-in-the-news\/hate-speech (accessed August 23, 2018)."},{"key":"key2021041510010531900_ref025","unstructured":"Compagnon, P. and Ollivier, K. (2017), \u201cGraph embeddings for social network analysis: state of the art\u201d, available at: www.researchgate.net\/publication\/331714802_Graph_Embeddings_for_Social_Network_Analysis_State_of_the_Art (accessed April 19, 2019)."},{"key":"key2021041510010531900_ref026","article-title":"Automated hate speech detection and the problem of offensive language","year":"2017"},{"issue":"11\/12","key":"key2021041510010531900_ref027","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1108\/01443330810915251","article-title":"Islamophobia: examining causal links between the media and \u2018race hate\u2019 from \u2018below\u2019","volume":"28","year":"2008","journal-title":"International Journal of Sociology and Social Policy"},{"key":"key2021041510010531900_ref028","first-page":"69","article-title":"Deep convolutional neural networks for sentiment analysis of short texts","year":"2014"},{"issue":"3","key":"key2021041510010531900_ref029","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1109\/MIS.2018.033001419","article-title":"OntoSenticNet: a commonsense ontology for sentiment analysis","volume":"33","year":"2018","journal-title":"IEEE Intelligent Systems"},{"issue":"45-60","key":"key2021041510010531900_ref030","first-page":"16","article-title":"Basic emotions","volume":"98","year":"1999","journal-title":"Handbook of Cognition and Emotion"},{"issue":"3","key":"key2021041510010531900_ref031","doi-asserted-by":"publisher","first-page":"406","DOI":"10.2495\/DNE-V11-N3-406-415","article-title":"Cyber hate speech on twitter: analyzing disruptive events from social media to build a violent communication and hate speech taxonomy","volume":"11","year":"2016","journal-title":"International Journal of Design & Nature and Ecodynamics"},{"key":"key2021041510010531900_ref032","volume-title":"Social Network and Sentiment Analysis on Twitter: Towards a Combined Approach","year":"2015"},{"key":"key2021041510010531900_ref033","first-page":"85","article-title":"Using convolutional neural networks to classify hate-speech","year":"2017"},{"key":"key2021041510010531900_ref034","first-page":"437","article-title":"Sentiment analysis of twitter data using machine learning approaches and semantic analysis","year":"2014"},{"issue":"16","key":"key2021041510010531900_ref035","doi-asserted-by":"crossref","first-page":"6266","DOI":"10.1016\/j.eswa.2013.05.057","article-title":"Twitter brand sentiment analysis: a hybrid system using n-gram analysis and dynamic artificial neural network","volume":"40","year":"2013","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"key2021041510010531900_ref036","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1108\/JICES-06-2015-0016","article-title":"Free vs hate speech on social media: the Indian perspective","volume":"14","year":"2016","journal-title":"Journal of Information, Communication & Ethics in Society"},{"key":"key2021041510010531900_ref037","unstructured":"Jennifer, G. (2015), \u201cIntroduction to social media investigation\u201d, available at: www.sciencedirect.com\/topics\/computer-science\/egocentric-network (accessed December 22, 2018)."},{"key":"key2021041510010531900_ref038","first-page":"304","volume-title":"The Harm in Hate Speech","year":"2012"},{"key":"key2021041510010531900_ref039","unstructured":"Joel, J. (2012), \u201cEthnopaulism and ethno-religious hate speech in Nigeria enabling policies for responding to \u2018hate speech\u2019 in Practice, 2012\u201d, available at: e-learning.ceu.hu\/user\/view.php?id=4190&course=1181 (accessed November 18, 2018)."},{"key":"key2021041510010531900_ref040","doi-asserted-by":"crossref","unstructured":"Kontopoulos, E., Berberidis, C., Dergiades, T. and Bassiliades, N. (2013), \u201cOntology-based sentiment analysis of Twitter posts, expert systems with applications (2013)\u201d, available at: http:\/\/dx.doi.org\/10.1016\/j.eswa.2013.01.001 (accessed March 18, 2019).","DOI":"10.1016\/j.eswa.2013.01.001"},{"key":"key2021041510010531900_ref041","article-title":"Locate the hate: detecting tweets against blacks","year":"2013"},{"key":"key2021041510010531900_ref042","unstructured":"Leondro, S., Maniack, M., Denzil, C., Fabricro, B. and Lngmar, W. (2016), \u201cAnalyzing the targets of hate in online social media\u201d, available at: https:\/\/arxiv.org\/pdf\/1603.07709.pdf (accessed November 6, 2018)."},{"key":"key2021041510010531900_ref043","doi-asserted-by":"crossref","unstructured":"Lettieri, N., Altamura, A., Malandrino, D. and Punzo, V. (2017), \u201cAgents shaping networks shaping agents: integrating social network analysis and agent-based modeling in computational crime research\u201d, in Oliveira, E., Gama, J., Vale, Z. and Lopes Cardoso, H. (Eds), Progress in Artificial Intelligence, EPIA 2017, Vol. 10423, Lecture Notes in Computer Science, Springer, Cham.","DOI":"10.1007\/978-3-319-65340-2_2"},{"key":"key2021041510010531900_ref044","volume-title":"Hate Crimes: The Rising Tide of Bigotry and Bloodshed","year":"1993"},{"key":"key2021041510010531900_ref045","unstructured":"Liu, B. and Zhang, L. (2012), \u201cA survey of opinions mining and sentiment analysis in mining text data\u201d, in Aggarwal, C. and Zhai, C. (Eds), Mining Text Data, Springer, Boston, MA, pp. 415-463."},{"issue":"8","key":"key2021041510010531900_ref046","first-page":"2257","article-title":"Attributed social network embedding","volume":"14","year":"2017","journal-title":"Journal of Latex Class Files"},{"key":"key2021041510010531900_ref085","unstructured":"Maina, K. (2010), \u201cSpeech, power and violence: hate speech and the political crisis in Kenya\u201d, available at: www.ushmm.org\/m\/pdfs\/20100423-speech-power-violence-kiai.pdf (accessed December 22, 2018)."},{"key":"key2021041510010531900_ref047","first-page":"467","article-title":"Detecting hate speech in social media","year":"2017"},{"issue":"3","key":"key2021041510010531900_ref048","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1108\/ITP-09-2014-0198","article-title":"Exposure to online hate material and social trust among Finnish youth","volume":"28","year":"2015","journal-title":"Information Technology & People"},{"key":"key2021041510010531900_ref049","unstructured":"Mirigxin, Z. (2010), \u201cSocial network analysis: history, concepts, and research\u201d, in Furht, B. (Ed.), Handbook of Social Network Technologies & Applications, Springer, Boston, MA, pp. 3-21."},{"key":"key2021041510010531900_ref050","unstructured":"Murphy, J. (2017), \u201cA brief analysis of the free speech vs. Hate Speech Debate_Stand\u201d, available at: www.standleague.org\/blog\/a-brief-analysis-of-the-free-speech-vs-hatespeech-debate.html (accessed December 11, 2017)."},{"issue":"46","key":"key2021041510010531900_ref051","first-page":"2456","article-title":"Hate speech and free speech","volume":"27","year":"1992","journal-title":"Economic and Political Weekly"},{"issue":"3","key":"key2021041510010531900_ref052","first-page":"99","article-title":"Using Na\u00efve Bayes algorithm in detection of hate tweets","volume":"8","year":"2018","journal-title":"International Journal of Scientific and Research Publications"},{"key":"key2021041510010531900_ref011","doi-asserted-by":"crossref","unstructured":"Oksanen, A., Hawdon, J., Holkeri, E., N\u00e4si, M. and R\u00e4s\u00e4nen, P. (2014), \u201cExposure to online hate among young social media users\u201d, Soul of Society: A Focus on the Lives of Children & Youth, Sociological Studies of Children and Youth, Vol. 18, Emerald Group Publishing Limited, pp. 253-273, https:\/\/doi.org\/10.1108\/S1537-466120140000018021","DOI":"10.1108\/S1537-466120140000018021"},{"key":"key2021041510010531900_ref053","doi-asserted-by":"publisher","DOI":"10.1080\/19331681.2016.1214094","article-title":"Can social media reveal the preferences of voters? A comparison between sentiment analysis and traditional opinion polls","year":"2016","journal-title":"Journal of Information Technology & Politics"},{"issue":"1-2","key":"key2021041510010531900_ref088","first-page":"1","article-title":"Opinion mining and sentiment analysis","volume":"2","year":"2008","journal-title":"Foundations and Trends\u00ae in Information Retrieval"},{"key":"key2021041510010531900_ref054","article-title":"Hate speech, machine classification and statistical modelling of information flows on twitter: interpretation and communication for policy decision making","year":"2014\/2017"},{"key":"key2021041510010531900_ref055","first-page":"151","article-title":"Social network analysis: selected methods and applications","volume-title":"Proceedings of the Dateso 2012 Workshop","year":"2012"},{"issue":"3","key":"key2021041510010531900_ref056","doi-asserted-by":"publisher","first-page":"163","DOI":"10.2989\/16073610209486308","article-title":"Perverts and sodomites: homophobia as hate speech in Africa","volume":"20","year":"2002","journal-title":"Southern African Linguistics and Applied Language Studies"},{"key":"key2021041510010531900_ref057","unstructured":"Renard, M. (2018), \u201cDoing your first sentiment analysis in R with sentimentr Oct 2, 2018\u201d, available at: https:\/\/medium.com\/@mattifuchs\/doing-your-first-sentiment-analysis-in-r-with-sentimentr-167855445132 (accessed January 16, 2019)."},{"key":"key2021041510010531900_ref058","first-page":"314","article-title":"A user independent, biosignal based, emotion recognition method","year":"2007"},{"key":"key2021041510010531900_ref059","unstructured":"Ring, C.E. (2013), \u201cHate speech in social media: an exploration of the problem and its proposed solutions\u201d, Journalism & Mass Communication Graduate Theses & Dissertations No. 15, available at: https:\/\/scholar.colorado.edu\/jour_gradetds\/15 (accessed December 2018)."},{"key":"key2021041510010531900_ref060","unstructured":"Rinker, T. (2019), \u201cDictionary based sentiment analysis that considers valence shifters\u201d, available at: https:\/\/github.com\/trinker\/sentimentr (accessed April 22, 2019)."},{"key":"key2021041510010531900_ref062","article-title":"Contextual semantics for sentiment analysis of Twitter","year":"2015"},{"issue":"1","key":"key2021041510010531900_ref061","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1016\/j.ipm.2015.01.005","article-title":"Contextual semantics for sentiment analysis of Twitter","volume":"52","year":"2016","journal-title":"Information Processing & Management"},{"key":"key2021041510010531900_ref100","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.ins.2015.03.040","article-title":"Sentiment analysis: a review and comparative analysis of web services","volume":"311","year":"2015","journal-title":"Information Sciences"},{"key":"key2021041510010531900_ref063","article-title":"Monitoring and tagging hate speech in social media","year":"2018"},{"key":"key2021041510010531900_ref064","unstructured":"Stanley, W. and Katherine, F. (1994), \u201cSocial network analysis in the social & behavioral science\u201d, in Wasserman, S. and Galaskiewicz, J. (Eds), Social Network Analysis: Methods & Applications, ISBN 9780521387071, Cambridge University Press and Sage Publications, London, pp. 1-27."},{"key":"key2021041510010531900_ref065","first-page":"1555","article-title":"Learning sentiment-specific word embedding for twitter sentiment classification","year":"2014"},{"issue":"4","key":"key2021041510010531900_ref073","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1111\/j.1468-2230.2006.00599.x","volume":"69","author":"The Modern Law Review","year":"2006","journal-title":"The Modern Law Review"},{"key":"key2021041510010531900_ref066","unstructured":"The Nation Nigeria (2017), \u201cHate speech\u201d, Editorial, August 30, available at: https:\/\/thenationonlineng.net\/hate-speech\/ (accessed April 2018)."},{"issue":"5","key":"key2021041510010531900_ref067","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1108\/K-06-2017-0229","article-title":"A proposed scheme for sentiment analysis: effective feature reduction based on statistical information of SentiWordNet","volume":"47","year":"2018","journal-title":"Kybernetes"},{"key":"key2021041510010531900_ref068","first-page":"124","volume-title":"Artificial Intelligence with Prolog Programming","year":"2011"},{"issue":"4","key":"key2021041510010531900_ref092","first-page":"481","article-title":"Determining social media influences of the politics of developing countries using social network analytics","volume":"50","year":"2016","journal-title":"Emerald Insight Program: Electronic Library and Information Systems"},{"key":"key2021041510010531900_ref069","article-title":"Node embeddings in social network analysis","year":"2015"},{"key":"key2021041510010531900_ref070","volume-title":"Hate Speech: The History of an American Controversy","year":"1994"},{"key":"key2021041510010531900_ref071","first-page":"138","article-title":"Are you a racist or am I seeing things? Annotator influence on hate speech detection on twitter","year":"2016"},{"key":"key2021041510010531900_ref072","first-page":"88","article-title":"Hateful symbols or hateful people? Predictive features for hate speech detection on twitter","year":"2016"},{"key":"key2021041510010531900_ref094","first-page":"60","article-title":"A survey on the role of negation in sentiment analysis","year":"2010"},{"key":"key2021041510010531900_ref101","article-title":"Towards an ethical framework for publishing Twitter data in social research: taking into account users\u2019 views, online context and algorithmic estimation","year":"2017","journal-title":"Sociology"},{"issue":"1","key":"key2021041510010531900_ref074","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.amepre.2013.02.025","article-title":"A practical approach for content mining of Tweets","volume":"45","year":"2013","journal-title":"American Journal of Preventive Medicine"},{"key":"key2021041510010531900_ref017","unstructured":"Yuan, B. (2017), \u201cSentiment analytics: lexicons construction and analysis\u201d, Masters Theses No. 7668, available at: https:\/\/scholarsmine.mst.edu\/masters_theses\/7668 (accessed April 18, 2019)."},{"issue":"5","key":"key2021041510010531900_ref075","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MIS.2005.96","article-title":"US domestic extremist groups on the web: link and content analysis","volume":"20","year":"2005","journal-title":"IEEE Intelligent Systems"},{"issue":"1","key":"key2021041510010531900_ref076","first-page":"46","article-title":"Identification of hatred speeches on Twitter","volume":"4","year":"2017","journal-title":"International Journal of Advances in Electronics and Computer Science"},{"key":"key2021041510010531900_ref078","article-title":"Hate speech, machine classification and statistical modeling of information flows on twitter: interpretation and communication for policy decision making","year":"2014"},{"key":"key2021041510010531900_ref079","first-page":"92697","volume-title":"\u201cSocial Network Analysis: A Methodological Introduction\u201d. Department of Sociology and Institute for Mathematical Behavioral Sciences","year":"2000"},{"issue":"1","key":"key2021041510010531900_ref081","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1002\/aris.1440380107","article-title":"Web mining: machine learning for web applications","volume":"38","year":"2004","journal-title":"Annual Review of Information Science and Technology"},{"key":"key2021041510010531900_ref087","unstructured":"Package \u201csentimentr\u201d (2019), \u201cCalculate text polarity sentiment\u201d, version 2.7.1, available at: https:\/\/cran.r-project.org\/web\/packages\/sentimentr\/sentimentr.pdf (accessed March 22, 2019)."},{"issue":"2","key":"key2021041510010531900_ref089","doi-asserted-by":"publisher","DOI":"10.3389\/fdata.2019.00002","article-title":"Deep representation learning for social network analysis","volume":"2","year":"2019","journal-title":"Frontiers in Big Data"},{"key":"key2021041510010531900_ref090","unstructured":"Rinker, T.W. (2018), \u201cSentimentr: calculate text polarity sentiment\u201d, version 2.6.1, available at: http:\/\/github.com\/trinker\/sentimentr (accessed April 22, 2019)."},{"key":"key2021041510010531900_ref091","volume-title":"Networks In and Around Organization","year":"2003"},{"key":"key2021041510010531900_ref093","first-page":"ix","volume-title":"Hate Speech","year":"1995"}],"container-title":["Data Technologies and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-01-2019-0007\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/DTA-01-2019-0007\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T23:14:53Z","timestamp":1753398893000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/dta\/article\/53\/4\/501-527\/99489"}},"subtitle":["A Twitter ego lexalytics approach"],"short-title":[],"issued":{"date-parts":[[2019,9,13]]},"references-count":92,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,9,13]]},"published-print":{"date-parts":[[2019,10,22]]}},"alternative-id":["10.1108\/DTA-01-2019-0007"],"URL":"https:\/\/doi.org\/10.1108\/dta-01-2019-0007","relation":{},"ISSN":["2514-9288"],"issn-type":[{"value":"2514-9288","type":"print"}],"subject":[],"published":{"date-parts":[[2019,9,13]]}}}