Isabel M. Kloumann

Temporal Patterns of Happiness and Information in a Global Social Network: Hedonometrics and Twitter

Individual happiness is a fundamental societal metric. Normally measured through self-report, happiness has often been indirectly characterized and overshadowed by more readily quantifiable economic indicators such as gross domestic product. Here, we examine expressions made on the online, global microblog and social networking service Twitter, uncovering and explaining temporal variations in happiness and information levels over timescales ranging from hours to years. Our data set comprises over 46 billion words contained in nearly 4.6 billion expressions posted over a 33 month span by over 63 million unique users. In measuring happiness, we construct a tunable, real-time, remote-sensing, and non-invasive, text-based hedonometer. In building our metric, made available with this paper, we conducted a survey to obtain happiness evaluations of over 10,000 individual words, representing a tenfold size improvement over similar existing word sets. Rather than being ad hoc, our word list is chosen solely by frequency of usage, and we show how a highly robust and tunable metric can be constructed and defended.

Human language reveals a universal positivity bias

Significance The most commonly used words of 24 corpora across 10 diverse human languages exhibit a clear positive bias, a big data confirmation of the Pollyanna hypothesis. The study’s findings are based on 5 million individual human scores and pave the way for the development of powerful language-based tools for measuring emotion. Using human evaluation of 100,000 words spread across 24 corpora in 10 languages diverse in origin and culture, we present evidence of a deep imprint of human sociality in language, observing that (i) the words of natural human language possess a universal positivity bias, (ii) the estimated emotional content of words is consistent between languages under translation, and (iii) this positivity bias is strongly independent of frequency of word use. Alongside these general regularities, we describe interlanguage variations in the emotional spectrum of languages that allow us to rank corpora. We also show how our word evaluations can be used to construct physical-like instruments for both real-time and offline measurement of the emotional content of large-scale texts.

Twitter reciprocal reply networks exhibit assortativity with respect to happiness

Abstract The advent of social media has provided an extraordinary, if imperfect, ‘big data’ window into the form and evolution of social networks. Based on nearly 40 million message pairs posted to Twitter between September 2008 and February 2009, we construct and examine the revealed social network structure and dynamics over the time scales of days, weeks, and months. At the level of user behavior, we employ our recently developed hedonometric analysis methods to investigate patterns of sentiment expression. We find users’ average happiness scores to be positively and significantly correlated with those of users one, two, and three links away. We strengthen our analysis by proposing and using a null model to test the effect of network topology on the assortativity of happiness. We also find evidence that more well connected users write happier status updates, with a transition occurring around Dunbars number. More generally, our work provides evidence of a social sub-network structure within Twitter and raises several methodological points of interest with regard to social network reconstructions.

Positivity of the English Language

Over the last million years, human language has emerged and evolved as a fundamental instrument of social communication and semiotic representation. People use language in part to convey emotional information, leading to the central and contingent questions: (1) What is the emotional spectrum of natural language? and (2) Are natural languages neutrally, positively, or negatively biased? Here, we report that the human-perceived positivity of over 10,000 of the most frequently used English words exhibits a clear positive bias. More deeply, we characterize and quantify distributions of word positivity for four large and distinct corpora, demonstrating that their form is broadly invariant with respect to frequency of word use.

Block models and personalized PageRank

Significance Methods based on PageRank have been fundamental to work on identifying communities in networks, but, to date, there has been little formal basis for the effectiveness of these methods. We establish a surprising connection between the personalized PageRank algorithm and the stochastic block model for random graphs, showing that personalized PageRank, in fact, provides the optimal geometric discriminant function for separating the communities in stochastic block models over a wide class of functions. Building on this result, we develop stronger classifiers that, although scalable, are competitive with computationally much more demanding methods such as belief propagation. Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods through the “seed set expansion problem”: given a subset S of nodes from a community of interest in an underlying graph, can we reliably identify the rest of the community? We start from the observation that the most widely used techniques for this problem, personalized PageRank and heat kernel methods, operate in the space of “landing probabilities” of a random walk rooted at the seed set, ranking nodes according to weighted sums of landing probabilities of different length walks. Both schemes, however, lack an a priori relationship to the seed set objective. In this work, we develop a principled framework for evaluating ranking methods by studying seed set expansion applied to the stochastic block model. We derive the optimal gradient for separating the landing probabilities of two classes in a stochastic block model and find, surprisingly, that under reasonable assumptions the gradient is asymptotically equivalent to personalized PageRank for a specific choice of the PageRank parameter α that depends on the block model parameters. This connection provides a formal motivation for the success of personalized PageRank in seed set expansion and node ranking generally. We use this connection to propose more advanced techniques incorporating higher moments of landing probabilities; our advanced methods exhibit greatly improved performance, despite being simple linear classification rules, and are even competitive with belief propagation.

Reply to Garcia et al.: Common mistakes in measuring frequency-dependent word characteristics.

The concerns expressed by Garcia et al. (1) are misplaced due to a range of misconceptions about word usage frequency, word rank, and expert-constructed word lists such as LIWC (Linguist Inquiry and Word Count) (2). We provide a complete response in our papers online appendices (3). Garcia et al. (1) suggest that the set of function words in the LIWC dataset (2) show a wide spectrum of average happiness with positive skew (figure 1A in ref. 1) when, according to their interpretation, these words should exhibit a Dirac δ function located at neutral (havg = 5 on a 1–9 scale). However, many words tagged as function words in the LIWC dataset readily elicit an emotional response in raters as exemplified by “greatest” (havg = 7.26), “best” (havg = 7.26), “negative” (havg = 2.42), and “worst” (havg = 2.10). In our study (3), basic function words that are expected to be neutral, such as “the” (havg = 4.98) and “to” (havg = 4.98), were appropriately scored as such. Moreover, no meaningful statement about biases can be made for sets of words chosen without frequency of use properly incorporated.

Synchronization Phenomena in Modelocked Parametric Frequency Combs

We show that the modelocking dynamics in parametric frequency combs is equivalent to synchronization phenomena that occur in many physical systems as described by the Kuramoto model for coupled oscillators.