Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms

Mining opinion on social media microblogs presents opportunities to extract meaningful insight from the public from trending issues like the “yahoo-yahoo” which in Nigeria, is synonymous to cybercrime. In this study, content analysis of selected historical tweets from “yahoo-yahoo” hash-tag was conducted for sentiment and topic modelling. A corpus of 5500 tweets was obtained and pre-processed using a pre-trained tweet tokenizer while Valence Aware Dictionary for Sentiment Reasoning (VADER), Liu Hu method, Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and Multidimensional Scaling (MDS) graphs were used for sentiment analysis, topic modelling and topic visualization. Results showed the corpus had 173 unique tweet clusters, 5327 duplicates tweets and a frequency of 9555 for “yahoo”. Further validation using the mean sentiment scores of ten volunteers returned R and R2 of 0.8038 and 0.6402; 0.5994 and 0.3463; 0.5999 and 0.3586 for Human and VADER; Human and Liu Hu; Liu Hu and VADER sentiment scores, respectively. While VADER outperforms Liu Hu in sentiment analysis, LDA and LSI returned similar results in the topic modelling. The study confirms VADER’s performance on unstructured social media data containing non-English slangs, conjunctions, emoticons, etc. and proved that emojis are more representative of sentiments in tweets than the texts.publishedVersio

Counterspeech on Twitter: A Field Study

As hateful and extremist content proliferates online, 'counterspeech' is gaining currency as a means of diminishing it. No wonder: counterspeech doesn't impinge on freedom of expression and can be practiced by almost anyone, requiring neither law nor institutions. The idea that 'more speech' is a remedy for harmful speech has been familiar in liberal democratic thought at least since U.S. Supreme Court Justice Louis Brandeis declared it in 1927. We are still without evidence, however, that counterspeech actually diminishes harmful speech or its effects. This would be very hard to measure offline but is a bit easier online, where speech and responses to it are recorded. In this paper we make a modest start. Specifically we ask: in what forms and circumstances does counterspeech - which we define as a direct response to hateful or dangerous speech - favorably influence discourse and perhaps even behavior?To our knowledge, this is the first study of Internet users (not a government or organization) counterspeaking spontaneously on a public platform like Twitter. Our findings are qualitative and anecdotal, since reliable quantitative detection of hateful speech or counterspeech is a problem yet to be fully solved due to the wide variations in language employed, although we made progress, as reported in an earlier paper that was part of this project (Saleem, Dillon, Benesch, & Ruths, 2016).We have identified four categories or "vectors" in each of which counterspeech functions quite differently, as hateful speech also does: one-to-one exchanges, many-to-one, one-to-many, and many-to-many. We also present a set of counterspeech strategies extrapolated from our data, with examples of tweets that illustrate those strategies at work, and suggestions for which ones may be successful

Detecting cyberbullying and cyberaggression in social media

Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been exposed to such prolonged and/or coordinated digital harassment. Victims can experience a wide range of emotions, with negative consequences such as embarrassment, depression, isolation from other community members, which embed the risk to lead to even more critical consequences, such as suicide attempts. In this work, we take the first concrete steps to understand the characteristics of abusive behavior in Twitter, one of today’s largest social media platforms. We analyze 1.2 million users and 2.1 million tweets, comparing users participating in discussions around seemingly normal topics like the NBA, to those more likely to be hate-related, such as the Gamergate controversy, or the gender pay inequality at the BBC station. We also explore specific manifestations of abusive behavior, i.e., cyberbullying and cyberaggression, in one of the hate-related communities (Gamergate). We present a robust methodology to distinguish bullies and aggressors from normal Twitter users by considering text, user, and network-based attributes. Using various state-of-the-art machine-learning algorithms, we classify these accounts with over 90% accuracy and AUC. Finally, we discuss the current status of Twitter user accounts marked as abusive by our methodology and study the performance of potential mechanisms that can be used by Twitter to suspend users in the future

Empathy Gap in Social Media Comments for Sexual Harassment Victim

Indonesian Twitter users who filed a complaint about sexual harassment were studied to see if there was an empathy gap among their tweet responses. The author uses a content analysis method and observes a sample of empathy gap experiences to notice and study empathy gap behaviour in Twitter toward sexual harassment victims. In the research that has been done, the comments tweet as amount 3733 tweets and chosen 60 of them randomly to know-how is the empathy gap with sexual harassment cases. It is concluded that bullies have aggressive and intimidating characteristics. On message production by the bully, actors are supposed to produce messages in expressive, conventional, and rhetorical ways, including negative empathy characteristics. So on the other hand, the research that has been done concludes that people who act as victims have passive and defensive elements. On message reception by the communicant (victim), the victim placed the position of receiving the message in a dominant, negotiating, and oppositional position

Study of aggressive behavior on social media

Recently, the expression of aggression in social networks has increased a lot, which also causes a lot of adverse effects, such as mental health problems or some other controversies. Hence we perform the first ever user aggressive behavior analysis on Twitter social media official microblogging site, which has no restriction on aggressive behavior. Using the proposed pipeline, we study the user’s aggressive behavior. The pipeline is based on three stages such as data collection, aggression detection, and user profiling. In this study, we detailed analyzed the aggressive behavior of users are depends on their aggressive feeds and events. Further, our analysis revealed that user engagement is higher in aggressive posts

Prédiction de la détérioration du comportement à l’aide de l’apprentissage automatique

Les plateformes de médias sociaux rassemblent des individus pour interagir de manière amicale et civilisée tout en ayant des convictions et des croyances diversifiées. Certaines personnes adoptent des comportements répréhensibles qui nuisent à la sérénité et affectent négativement l’équanimité des autres utilisateurs. Certains cas de mauvaise conduite peuvent initialement avoir de petits effets statistiques, mais leur accumulation persistante pourrait entraîner des conséquences majeures et dévastatrices. L’accumulation persistante des mauvais comportements peut être un prédicteur valide des facteurs de risque de détérioration du comportement. Le problème de la détérioration du comportement n’a pas été largement étudié dans le contexte des médias sociaux. La détection précoce de la détérioration du comportement peut être d’une importance cruciale pour éviter que le mauvais comportement des individus ne s’aggrave. Cette thèse aborde le problème de la détérioration du comportement dans le contexte des médias sociaux. Nous proposons de nouvelles méthodes basées sur l’apprentissage automatique qui (1) explorent les séquences comportementales et leurs motifs temporels pour faciliter la compréhension des comportements manifestés par les individus et (2) prédisent la détérioration du comportement à partir de combinaisons consécutives de motifs séquentiels correspondant à des comportements inappropriés. Nous menons des expériences approfondies à l’aide d’ensembles de données du monde réel et démontrons la capacité de nos modèles à prédire la détérioration du comportement avec un haut degré de précision, c’est-à-dire des scores F-1 supérieurs à 0,8. En outre, nous examinons la trajectoire de détérioration du comportement afin de découvrir les états émotionnels que les individus présentent progressivement et d’évaluer si ces états émotionnels conduisent à la détérioration du comportement au fil du temps. Nos résultats suggèrent que la colère pourrait être un état émotionnel potentiel qui pourrait contribuer substantiellement à la détérioration du comportement