Understanding the behaviour and influence of automated social agents

Soft-bound submitted: Fri 23 Feb 2018 Corrections submitted: Mon 30 Jul 2018 Corrections approved: Tue 7 Aug 2018 Apollo submitted: Wed 22 Aug 2018 Hard-bound submitted: Fri 24 Aug 2018Online social networks (OSNs) have seen a remarkable rise in the presence of automated social agents, or social bots. Social bots are the new computing viral, that are surreptitious and clever. What facilitates the creation of social agents is the massive human user-base and business-supportive operating model of social networks. These automated agents are injected by agencies, brands, individuals, and corporations to serve their work and purpose; utilising them for news and emergency communication, marketing, social activism, political campaigning, and even spam and spreading malicious content. Their influence was recently substantiated by coordinated social hacking and computational political propaganda. The thesis of my dissertation argues that automated agents exercise a profound impact on OSNs that transforms into an array of influence on our society and systems. However, latent or veiled, these agents can be successfully detected through measurement, feature extraction and finely tuned supervised learning models. The various types of automated agents can be further unravelled through unsupervised machine learning and natural language processing, to formally inform the populace of their existence and impact.Sep'14-Aug'17, Marie Curie ITN METRICS, Early-Stage Researcher Sep'17, UMobile, Research Associate Oct'17-Mar'18, EPSRC Global Challenges Research Fund, Research Associat

Antecedents of retweeting in a (political) marketing context

Word of mouth disseminates across Twitter by means of retweeting; however the antecedents of retweeting have not received much attention. This study uses the CHAID decision tree predictive method (Kass, 1980) with readily available Twitter data, and manually coded sentiment and content data, to identify why some tweets are more likely to be retweeted than others in a (political) marketing context. The analysis includes four CHAID models: (i) using message structure variables only, (ii) source variables only, (iii) message content and sentiment variables only and (iv) a combined model using source, message structure, message content and sentiment variables. The aggregated predictive model correctly classified retweeting behavior with a 76.7% success rate. Retweeting tends to occur when the originator has a high number of Twitter followers and the sentiment of the tweet is negative, contradicting previous research (East, Hammond, & Wright, 2007; Wu, 2013) but concurring with others (Hennig-Thurau, Wiertz, & Feldhaus, 2014). Additionally, particular types of tweet content are associated with high levels of retweeting, in particular those tweets including fear appeals or expressing support for others, whilst others are associated with very low levels of retweeting, such as those mentioning the sender’s personal life. Managerial implications and research directions are presented. The study makes a methodological contribution by illustrating how CHAID predictive modelling can be used for Twitter data analysis and a theoretical contribution by providing insights into why retweeting occurs in a (political) marketing context

Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach

As the novel coronavirus spreads across the world, work, pleasure, entertainment, social interactions, and meetings have shifted online. The conversations on social media have spiked, and given the uncertainties and new policies, COVID-19 remains the trending topic on all such platforms, including Twitter. This research explores the factors that affect COVID-19 content-sharing by Twitter users. The analysis was conducted using 57,000 plus tweets that mentioned COVID-19 and related keywords. The tweets were subjected to the Natural Language Processing (NLP) techniques like Topic modelling, Named Entity-Relationship, Emotion & Sentiment analysis, and Linguistic feature extraction. These methods generated features that could help explain the retweet count of the tweets. The results indicate that tweets with named entities (person, organisation, and location), expression of negative emotions (anger, disgust, fear, and sadness), reference to mental health, optimistic content, and greater length have higher chances of being shared (retweeted). On the other hand, tweets with more hashtags and user mentions are less likely to be shared

Analiza raspoloženja tvitova predsjednika Trumpa: od pobjede na izborima do borbe protiv COVID-19

Twitter, as one of the popular social networks today and big data generator, can affect and change the public discourse, so political candidates are using it extensively as the vehicle for attracting and keeping their followers. Since Donald Trump\u27s 2016 presidency election, his Twitter account with millions of followers has become an important subject for various statistical analyses, mostly because of his controversy. Therefore, this paper uses sentiment analysis of a large set of his tweets to explore his influence, as well the set of affective and cognitive aspects of his messages. The results of this analysis indicate what kind of findings in political domain can be recognized from tweets, and how they can be interpreted.Twitter, kao jedna od popularnih društvenih mreža današnjice i generator velikih podataka, može utjecati i mijenjati javni diskurs, pa ga politički kandidati intenzivno koriste kao sredstvo za privlačenje i održavanje pinga svojih pratitelja. Od predsjedničkih izbora Donalda Trumpa 2016., njegov Twitter račun s milijunima pratitelja postao je važan predmet raznih statističkih analiza, ponajviše zbog njegove kontroverze. Stoga ovaj rad koristi analizu sentimenta velikog skupa njegovih tweetova kako bi istražio njegov utjecaj, kao i skup afektivnih i kognitivnih aspekata njegovih poruka. Rezultati ove analize ukazuju na to kakva se saznanja u političkoj domeni mogu prepoznati iz tweetova i kako ih se može interpretirati

Can environmental citizenship be enhanced through social media? A case study of engagement in a UK University

The research presented in this thesis focuses around the question: “can social media tools be used effectively to foster a participatory process that increases environmental citizenship and promote pro-environmental behaviour-change?”. The research aims to understand the role of staff and students in the socio-technical system that influences an institution’s environmental impact. Users need not to be educated, but empowered in order to be able to take decisions that would reduce the environmental impact of their institutions. Therefore a participatory process is suggested as the right tool to nurture environmental citizens, who will be able to take ‘right’ and ‘good’ decisions about their pro-environmental actions. In the last years, social media have emerged as a worldwide phenomenon. But alongside the grand claims of a social media inspired ‘revolution’ lie more nuanced questions around the role of digital tools in ‘every day’ contexts, and whether or not they are facilitating a cultural change or merely adding to the noise of modern life. The thesis contributes to the debate through presenting findings from an action research study at an East Midlands University in which a case study approach was implemented to explore the potentialities offered by participating in decision-making regarding pro-environmental issues in the institutional context, as they are mediated by social media. To generate behaviour-change the two correlated theories of public engagement and environmental citizenship were tested. Findings indicate that behaviour change and enhanced environmental citizenship are achievable through participation using social media, as several interviewees reported a change or a reinforcement of already existing pro-environmental behaviours as a consequence of the campaign. However, the reported changes were minor and it is difficult to advocate that they could noticeably contribute to the requested reduction targets on carbon emission from behaviour-change of the HE sector