Timescales of Massive Human Entrainment

The past two decades have seen an upsurge of interest in the collective behaviors of complex systems composed of many agents entrained to each other and to external events. In this paper, we extend concepts of entrainment to the dynamics of human collective attention. We conducted a detailed investigation of the unfolding of human entrainment - as expressed by the content and patterns of hundreds of thousands of messages on Twitter - during the 2012 US presidential debates. By time locking these data sources, we quantify the impact of the unfolding debate on human attention. We show that collective social behavior covaries second-by-second to the interactional dynamics of the debates: A candidate speaking induces rapid increases in mentions of his name on social media and decreases in mentions of the other candidate. Moreover, interruptions by an interlocutor increase the attention received. We also highlight a distinct time scale for the impact of salient moments in the debate: Mentions in social media start within 5-10 seconds after the moment; peak at approximately one minute; and slowly decay in a consistent fashion across well-known events during the debates. Finally, we show that public attention after an initial burst slowly decays through the course of the debates. Thus we demonstrate that large-scale human entrainment may hold across a number of distinct scales, in an exquisitely time-locked fashion. The methods and results pave the way for careful study of the dynamics and mechanisms of large-scale human entrainment.Comment: 20 pages, 7 figures, 6 tables, 4 supplementary figures. 2nd version revised according to peer reviewers' comments: more detailed explanation of the methods, and grounding of the hypothese

BREXIT: A granger causality of twitter political polarisation on the FTSE 100 Index and the Pound

BREXIT is the single biggest geopolitical event in British history since WWII. Whilst the political fallout has become a tragicomedy, the political ramifications has had a profound impact on the Pound and the FTSE 100 index. This paper examines Twitter political discourse surrounding the BREXIT withdrawal agreement. In particular we focus on the discussions around four different exit strategies known as “Norway”, “Article 50”, the“Backstop” and “No Deal” and their effect on the pound and FTSE 100 index from the period of rumblings of the cancellation of the Meaning Vote on December 10th 2018 inclusive of second defeat on the Prime Minister’s BREXIT exit strategy on February 14th to February 24th 2019. Our approach focuses on using a Naive Bayes classification algorithm to assess political party and public Twitter sentiment. A Granger causality analysis is then introduced to investigate the hypothesis that BREXIT political and public sentiment, as measured by the twitter sentiment time series, is indicative of changes in the GBP/EUR Fx and FTSE 100 Index. Our results indicate that the accuracy of the “Article 50” scenario had the single biggest effect on short run dynamics on the FTSE 100 index, additionally the “Norway” BREXIT strategy has a marginal effect on the FTSE 100 index whilst there was no significant causation to the GBP/EUR Fx

Прогнозирование политических предпочтений в социальных сетях (на материале ВКонтакте)

The authors hypothesize that textual information posted on personal pages on social media reflects the political views of users to some extent. Therefore, this textual information can be used to predict political views on social media. The authors conduct experiments on textual data from user pages and test two machine learning methods to classify pages that declare different political preferences. To undertake a study, the authors collected anonymous open textual data of users of the VKontakte social network (the number of pages is 10 123). Data collection was carried out using the VKontakte Application Programming Interface (VK API). As a result of the analysis of the collected data, the authors discovered two types of textual information. The first is a text filled by the user by selecting one of several possible values (binary or categorical variables). The field “Political Views” is one of these text fields, it provides nine options for selection. The second type of text information includes information entered by the user in an arbitrary form (interests, activities, etc.). The authors trained and tested two machine learning models to predict users’ political views based on the remaining text information from their pages: a) linear support vector classifier using text representations from the bag-of-words model; b) neural network using Multilingual BERT text embeddings. The results show that the models sufficiently successfully perform binary classification of users who have polar political views (for example, communists – libertarians, communists – ultra-conservatives). Nevertheless, the results for the groups of users that have close political views are significantly lower. In addition, the authors investigated the assumption that users often indicate “indifferent” political views as “moderate”. The authors classified the groups of users who declare indifferent or moderate views (the two largest categories in our dataset) and users who indicated other political preferences. The results demonstrate a sufficiently high performance for the classification of custom pages based on these two political views

Incivility in 2022 Senatorial Elections

This honors capstone project will examine the effect of social media, specifically Twitter, on U.S. senate elections in 2022. It will track the tweets of personal and official campaign Twitter accounts from the end of the primary until election night in two “Toss Up” or highly contested seats in the 2022 senate elections. This project will examine the winner of the Republican and Democrat primaries only. All the tweets from the timeframe will be tracked and categorized by intention or use of the tweet. These categories will break down the tweet into what it was meant to do be it campaigning, informing, or messaging. The research will break the tweets down by type and compare it to the trends seen in traditional media such as print, signage, radio, television, and any website use. The conclusion will look to show the similarities and differences in style of the candidates and the other forms of campaigning

Characterizing the personality of twitter users based on their timeline information

Personality is a set of characteristics that differentiate a person from others. It can be identified by the words that people use in conversations or in publications that they do in social networks. Most existing work focuses on personality prediction analyzing English texts. In this study we analyzed publications of the Portuguese users of the social network Twitter. Taking into account the difficulties in sentiment classification that can be caused by the 140 character limit imposed on tweets, we decided to use different features and methods such as the quantity of followers, friends, locations, publication times, etc. to get a more precise picture of a personality. In this paper, we present methods by which the personality of a user can be predicted without any effort from the Twitter users. The personality can be accurately predicted through the publicly available information on Twitter profiles.A personalidade traduz-se num conjunto de características que diferenciam uma pessoa de outras. Pode ser identificada pelas palavras que as pessoas usam numa conversa ou em publicações que fazem nas redes sociais. A maioria dos trabalhos existentes na literatura estão focados na previsão de personalidade analisando textos em Inglês. Neste estudo, foram analisadas publicações dos utilizadores Portugueses na rede social Twitter. Tendo em conta que o limite de 140 caracteres imposto aos tweets pode dificultar a classificação dos sentimentos dos textos produzidos, foi decidido usar diferentes características e métodos tais como locais, tempo de publicação, quantidade de seguidores, quantidade de amigos, etc., para obter uma imagem mais completa da personalidade. Este documento apresenta um método para fazer a previsão da personalidade de utilizadores do Twitter, com base na informação existente e sem qualquer esforço do lado desses utilizadores. A personalidade pode ser calculada através da informação pública disponível

Living in the past, present, and future: measuring temporal orientation with language

OBJECTIVE: Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring temporal orientation through language expressed on social media. METHOD: Judges rated the temporal orientation of 4,302 social media messages. We trained a classifier based on these ratings, which could accurately predict the temporal orientation of new messages in a separate validation set (accuracy/mean sensitivity = .72; mean specificity = .77). We used the classifier to automatically classify 1.3 million messages written by 5,372 participants (50% female, aged 13-48). Finally, we tested whether individual differences in past, present, and future orientation differentially related to gender, age, Big Five personality, satisfaction with life, and depressive symptoms. RESULTS: Temporal orientations exhibit several expected correlations with age, gender, and Big Five personality. More future-oriented people were older, more likely to be female, more conscientious, less impulsive, less depressed, and more satisfied with life; present orientation showed the opposite pattern. CONCLUSION: Language-based assessments can complement and extend existing measures of temporal orientation, providing an alternative approach and additional insights into language and personality relationships. This article is protected by copyright. All rights reserved.Support for this article was provided by grant #63597 from the Robert Wood Johnson Foundation (M. E. P. Seligman, PI) and by a grant from the Templeton Religion Trust (M.E.P. Seligman, H. A. Schwartz, L. H. Ungar, co-PIs)

Understanding violence through social media

While social media analysis has been widely utilized to predict various market and political trends, its utilization to improve geospatial conflict prediction in contested environments remains understudied. To determine the feasibility of social media utilization in conflict prediction, we compared historical conflict data and social media metadata, utilizing over 829,537 geo-referenced messages sent through the Twitter network within Iraq from August 2013 to July 2014. From our research, we conclude that social media metadata has a positive impact on conflict prediction when compared with historical conflict data. Additionally, we find that utilizing the most extreme negative terminology from a locally derived social media lexicon provided the most significant predictive accuracy for determining areas that would experience subsequent violence. We suggest future research projects center on improving the conflict prediction capability of social media data and include social media analysis in operational assessments.http://archive.org/details/understandingvio1094556920Major, United States ArmyLieutenant Commander, United States NavyApproved for public release; distribution is unlimited