Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as “social sensors.” Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We confirm that these findings also hold true for tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.     

Discovering Social Events through Online Attention

Twitter is a major social media platform in which users send and read messages (“tweets”) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible “thermostats” of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.     

Emoticon-Based Ambivalent Expression: A Hidden Indicator for Unusual Behaviors in Weibo

Recent decades have witnessed online social media being a big-data window for testifying conventional social theories quantitatively and exploring much detailed human behavioral patterns. In this paper, by tracing the emoticon use in Weibo, a group of hidden “ambivalent users” are disclosed for frequently posting ambivalent tweets containing both positive and negative emotions. Further investigation reveals that this ambivalent expression could be a novel indicator of many unusual social behaviors. For instance, ambivalent users with the female as the majority like to make a sound in midnights and at weekends. They mention their close friends frequently in ambivalent tweets, which attract more replies and serve as a more private communication way. Ambivalent users also respond differently to public affairs from others and demonstrate more interests in entertainment and sports events. Moreover, the sentiment shift in ambivalent tweets is more evident than usual and exhibits a clear “negative to positive” pattern. The above observations, though being promiscuous seemingly, actually point to the self-regulation of negative mood in Weibo, which could find its basis from the traditional emotion management theories in sociology but makes an important extension to the online environment in this study. Finally, as an interesting corollary, ambivalent users are found connected with compulsive buyers and turn out to be perfect targets for online marketing.     

Reading the Mind in the Eyes or Reading between the Lines? Theory of Mind Predicts Collective Intelligence Equally Well Online and Face-To-Face

Recent research with face-to-face groups found that a measure of general group effectiveness (called “collective intelligence”) predicted a group’s performance on a wide range of different tasks. The same research also found that collective intelligence was correlated with the individual group members’ ability to reason about the mental states of others (an ability called “Theory of Mind” or “ToM”). Since ToM was measured in this work by a test that requires participants to “read” the mental states of others from looking at their eyes (the “Reading the Mind in the Eyes” test), it is uncertain whether the same results would emerge in online groups where these visual cues are not available. Here we find that: (1) a collective intelligence factor characterizes group performance approximately as well for online groups as for face-to-face groups; and (2) surprisingly, the ToM measure is equally predictive of collective intelligence in both face-to-face and online groups, even though the online groups communicate only via text and never see each other at all. This provides strong evidence that ToM abilities are just as important to group performance in online environments with limited nonverbal cues as they are face-to-face. It also suggests that the Reading the Mind in the Eyes test measures a deeper, domain-independent aspect of social reasoning, not merely the ability to recognize facial expressions of mental states.     

National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.     

The Effects of Twitter Sentiment on Stock Price Returns

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known “event study” from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the “event study” methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1–2%), but the dependence is statistically significant for several days after the events.     

Evolutionary Establishment of Moral and Double Moral Standards through Spatial Interactions

Situations where individuals have to contribute to joint efforts or share scarce resources are ubiquitous. Yet, without proper mechanisms to ensure cooperation, the evolutionary pressure to maximize individual success tends to create a tragedy of the commons (such as over-fishing or the destruction of our environment). This contribution addresses a number of related puzzles of human behavior with an evolutionary game theoretical approach as it has been successfully used to explain the behavior of other biological species many times, from bacteria to vertebrates. Our agent-based model distinguishes individuals applying four different behavioral strategies: non-cooperative individuals (“defectors”), cooperative individuals abstaining from punishment efforts (called “cooperators” or “second-order free-riders”), cooperators who punish non-cooperative behavior (“moralists”), and defectors, who punish other defectors despite being non-cooperative themselves (“immoralists”). By considering spatial interactions with neighboring individuals, our model reveals several interesting effects: First, moralists can fully eliminate cooperators. This spreading of punishing behavior requires a segregation of behavioral strategies and solves the “second-order free-rider problem”. Second, the system behavior changes its character significantly even after very long times (“who laughs last laughs best effect”). Third, the presence of a number of defectors can largely accelerate the victory of moralists over non-punishing cooperators. Fourth, in order to succeed, moralists may profit from immoralists in a way that appears like an “unholy collaboration”. Our findings suggest that the consideration of punishment strategies allows one to understand the establishment and spreading of “moral behavior” by means of game-theoretical concepts. This demonstrates that quantitative biological modeling approaches are powerful even in domains that have been addressed with non-mathematical concepts so far. The complex dynamics of certain social behaviors become understandable as the result of an evolutionary competition between different behavioral strategies.     

Does Twitter Trigger Bursts in Signature Collections?

Introduction

The quantification of social media impacts on societal and political events is a difficult undertaking. The Japanese Society of Oriental Medicine started a signature-collecting campaign to oppose a medical policy of the Government Revitalization Unit to exclude a traditional Japanese medicine, “Kampo,” from the public insurance system. The signature count showed a series of aberrant bursts from November 26 to 29, 2009. In the same interval, the number of messages on Twitter including the keywords “Signature” and “Kampo,” increased abruptly. Moreover, the number of messages on an Internet forum that discussed the policy and called for signatures showed a train of spikes.

Methods and Findings

In order to estimate the contributions of social media, we developed a statistical model with state-space modeling framework that distinguishes the contributions of multiple social media in time-series of collected public opinions. We applied the model to the time-series of signature counts of the campaign and quantified contributions of two social media, i.e., Twitter and an Internet forum, by the estimation. We found that a considerable portion (78%) of the signatures was affected from either of the social media throughout the campaign and the Twitter effect (26%) was smaller than the Forum effect (52%) in total, although Twitter probably triggered the initial two bursts of signatures. Comparisons of the estimated profiles of the both effects suggested distinctions between the social media in terms of sustainable impact of messages or tweets. Twitter shows messages on various topics on a time-line; newer messages push out older ones. Twitter may diminish the impact of messages that are tweeted intermittently.

Conclusions

The quantification of social media impacts is beneficial to better understand people’s tendency and may promote developing strategies to engage public opinions effectively. Our proposed method is a promising tool to explore information hidden in social phenomena.     

Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data

Use of socially generated “big data” to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society''s reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between “real time monitoring” and “early predicting” remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.     

Roles of leader and follower cells in collective cell migration

Collective cell migration is a widely observed phenomenon during animal development, tissue repair, and cancer metastasis. Considering its broad involvement in biological processes, it is essential to understand the basics behind the collective movement. Based on the topology of migrating populations, tissue-scale kinetics, called the “leader–follower” model, has been proposed for persistent directional collective movement. Extensive in vivo and in vitro studies reveal the characteristics of leader cells, as well as the special mechanisms leader cells employ for maintaining their positions in collective migration. However, follower cells have attracted increasing attention recently due to their important contributions to collective movement. In this Perspective, the current understanding of the molecular mechanisms behind the “leader–follower” model is reviewed with a special focus on the force transmission and diverse roles of leaders and followers during collective cell movement.     

Animal‐borne video systems provide insight into the reproductive behavior of the Asian black bear

Previous studies on the mating system of the Asian black bear (Ursus thibetanus) have been limited to observations of captive populations and estimations of multiple paternities. Hence, the mating system of wild bears remains poorly understood. Animal‐borne camera systems (i.e., cameras mounted on animals) provide novel tools to study the behavior of elusive animals. Here, we used an animal‐borne video system to record the activities of wild bears during the mating season. Video camera collars were attached to four adult Asian black bears (male “A” and “B,” and female “A” and “B”) captured in Tokyo, central Japan, in May and June 2018. The collars were retrieved in July 2018, after which the video data were downloaded and analyzed in terms of bear activity and mating behavior. All the bears were found to interact with other uniquely identifiable bears for some of the time (range 9–22 days) during the deployment period (range 36–45 days), and multiple mating in males was documented. Both males and females exhibited different behaviors on social days (i.e., days when the bear interacted with conspecifics) compared with solitary days (i.e., days with no observed interactions with conspecifics). Compared with solitary days, the bears spent a lower proportion of time on foraging activities and higher proportion of time on resting activities on social days. Our results suggest that Asian black bears have a polygamous mating system, as both sexes consort and potentially mate with multiple partners during a given mating season. Furthermore, bears appeared to reduce their foraging activities on social days and engaged more in social interactions.     

Human Collective Intelligence under Dual Exploration-Exploitation Dilemmas

The exploration-exploitation dilemma is a recurrent adaptive problem for humans as well as non-human animals. Given a fixed time/energy budget, every individual faces a fundamental trade-off between exploring for better resources and exploiting known resources to optimize overall performance under uncertainty. Colonies of eusocial insects are known to solve this dilemma successfully via evolved coordination mechanisms that function at the collective level. For humans and other non-eusocial species, however, this dilemma operates within individuals as well as between individuals, because group members may be motivated to take excessive advantage of others'' exploratory findings through social learning. Thus, even though social learning can reduce collective exploration costs, the emergence of disproportionate “information scroungers” may severely undermine its potential benefits. We investigated experimentally whether social learning opportunities might improve the performance of human participants working on a “multi-armed bandit” problem in groups, where they could learn about each other''s past choice behaviors. Results showed that, even though information scroungers emerged frequently in groups, social learning opportunities reduced total group exploration time while increasing harvesting from better options, and consequentially improved collective performance. Surprisingly, enriching social information by allowing participants to observe others'' evaluations of chosen options (e.g., Amazon''s 5-star rating system) in addition to choice-frequency information had a detrimental impact on performance compared to the simpler situation with only the choice-frequency information. These results indicate that humans groups can handle the fundamental “dual exploration-exploitation dilemmas” successfully, and that social learning about simple choice-frequencies can help produce collective intelligence.     

Development and Evaluation of a Parenting Resilience Elements Questionnaire (PREQ) Measuring Resiliency in Rearing Children with Developmental Disorders

We developed a parenting resilience elements questionnaire (PREQ) measuring the degree to which mothers possess elements that aid in adapting to challenges and difficulties related to children with developmental disorders (DD). A total of 424 parents of children with DD were recruited from five medical institutes. Psychometric properties of PREQ were evaluated using data of 363 mothers of children with DD. Furthermore, multiple regression analysis was performed, predicting depressive symptoms and parenting behavior with PREQ subscales, a general health questionnaire, and the total difficulties score of a strength and difficulties questionnaire. Factor analysis revealed three reliable factors: “knowledge of the child’s characteristics,” “perceived social supports,” and “positive perceptions of parenting.” Moreover, multiple regression analysis showed that “knowledge of the child’s characteristics” was associated with parenting behavior, whereas “perceived social supports” predicted depressive symptoms; “positive perceptions of parenting” influenced both parenting behavior and depressive symptoms. These findings indicated that the PREQ may be used as a scale measuring resiliency in mothers of children with DD and is useful for evaluating their parenting ability in clinical interventions.     

Are British urban foxes (Vulpes vulpes) “bold”? The importance of understanding human–wildlife interactions in urban areas

Human–wildlife interactions are believed to be increasing in urban areas. In Britain, numerous media reports have stated that urban foxes (Vulpes vulpes) are becoming “bolder,” thereby posing a risk to public safety. However, such claims overlook how an individual''s personality might influence urban fox behavior. Personality determines multiple aspects of an animal''s interactions with both conspecifics and its environment, and can have a significant impact on how people perceive wildlife. Furthermore, describing urban foxes as “bold” confounds two different but inter‐related behaviors, both of which influence an animal''s propensity to take risks. Neophobia affects an animal''s reaction to novelty, wariness its reaction to potential threats. Since urban wildlife frequently encounters both novel and threatening stimuli, a highly adaptable species such as the red fox might be predicted to exhibit reduced neophobia and wariness. We investigated how social status influenced both behaviors in Bristol''s fox population. Dominant foxes were significantly more neophobic and warier than subordinates, which adopt a more exploratory and risk‐taking lifestyle to meet their energetic and other needs. We found no seasonal effect on neophobia and wariness, although this may be due to sample size. The presence of conspecifics decreased neophobia for dominants, and wariness for both dominants and subordinates. We highlight the importance of considering animal social status and personality when planning management protocols, since interventions that destabilize fox social groups are likely to increase the number of subordinate foxes in the population, thereby increasing rather than decreasing the number of interactions between humans and urban foxes.     

A Geometric Representation of Collective Attention Flows

With the fast development of Internet and WWW, “information overload” has become an overwhelming problem, and collective attention of users will play a more important role nowadays. As a result, knowing how collective attention distributes and flows among different websites is the first step to understand the underlying dynamics of attention on WWW. In this paper, we propose a method to embed a large number of web sites into a high dimensional Euclidean space according to the novel concept of flow distance, which both considers connection topology between sites and collective click behaviors of users. With this geometric representation, we visualize the attention flow in the data set of Indiana university clickstream over one day. It turns out that all the websites can be embedded into a 20 dimensional ball, in which, close sites are always visited by users sequentially. The distributions of websites, attention flows, and dissipations can be divided into three spherical crowns (core, interim, and periphery). 20% popular sites (Google.com, Myspace.com, Facebook.com, etc.) attracting 75% attention flows with only 55% dissipations (log off users) locate in the central layer with the radius 4.1. While 60% sites attracting only about 22% traffics with almost 38% dissipations locate in the middle area with radius between 4.1 and 6.3. Other 20% sites are far from the central area. All the cumulative distributions of variables can be well fitted by “S”-shaped curves. And the patterns are stable across different periods. Thus, the overall distribution and the dynamics of collective attention on websites can be well exhibited by this geometric representation.     

Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist

While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules (“majority”, “quorum”, and “weighted quorum”) when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.     

Role of the AP2 β-Appendage Hub in Recruiting Partners for Clathrin-Coated Vesicle Assembly

Adaptor protein complex 2 α and β-appendage domains act as hubs for the assembly of accessory protein networks involved in clathrin-coated vesicle formation. We identify a large repertoire of β-appendage interactors by mass spectrometry. These interact with two distinct ligand interaction sites on the β-appendage (the “top” and “side” sites) that bind motifs distinct from those previously identified on the α-appendage. We solved the structure of the β-appendage with a peptide from the accessory protein Eps15 bound to the side site and with a peptide from the accessory cargo adaptor β-arrestin bound to the top site. We show that accessory proteins can bind simultaneously to multiple appendages, allowing these to cooperate in enhancing ligand avidities that appear to be irreversible in vitro. We now propose that clathrin, which interacts with the β-appendage, achieves ligand displacement in vivo by self-polymerisation as the coated pit matures. This changes the interaction environment from liquid-phase, affinity-driven interactions, to interactions driven by solid-phase stability (“matricity”). Accessory proteins that interact solely with the appendages are thereby displaced to areas of the coated pit where clathrin has not yet polymerised. However, proteins such as β-arrestin (non-visual arrestin) and autosomal recessive hypercholesterolemia protein, which have direct clathrin interactions, will remain in the coated pits with their interacting receptors.     

Network Effects of Risk Behavior Change Following Prophylactic Interventions

We formulated a network-based model to understand how risk behavior change in conjunction with failure of prophylactic interventions can lead to unintended outcomes where “less (intervention) is more (effective).” Our model captures the distinction between one- and two-sided risk behavior change. In one-sided situations (e.g. influenza/H1N1) it is sufficient for either individual in an interaction to exhibit risk behavior change whereas in two-sided situations (e.g. AIDS/HIV) it is necessary for both individuals in the interaction to exhibit risk behavior change, for a potential transmission of the disease. A central discovery is that this phenomenon occurs at differing levels of intervention coverage depending upon the “sidedness” of the interaction. We find that for one-sided interactions, sufficiently high vaccination coverage is necessary for mitigating the effects of risk behavior; for two-sided interactions, it is essential to combine prophylactic treatments with programs aimed at reducing risky behavior. Furthermore, again dependent on the “sidedness,” targeting highly connected nodes can be strictly worse than uniformly random interventions at the same level of coverage.     

Social foraging in vampire bats is predicted by long-term cooperative relationships

Stable social bonds in group-living animals can provide greater access to food. A striking example is that female vampire bats often regurgitate blood to socially bonded kin and nonkin that failed in their nightly hunt. Food-sharing relationships form via preferred associations and social grooming within roosts. However, it remains unclear whether these cooperative relationships extend beyond the roost. To evaluate if long-term cooperative relationships in vampire bats play a role in foraging, we tested if foraging encounters measured by proximity sensors could be explained by wild roosting proximity, kinship, or rates of co-feeding, social grooming, and food sharing during 21 months in captivity. We assessed evidence for 6 hypothetical scenarios of social foraging, ranging from individual to collective hunting. We found that closely bonded female vampire bats departed their roost separately, but often reunited far outside the roost. Repeating foraging encounters were predicted by within-roost association and histories of cooperation in captivity, even when accounting for kinship. Foraging bats demonstrated both affiliative and competitive interactions with different social calls linked to each interaction type. We suggest that social foraging could have implications for social evolution if “local” within-roost cooperation and “global” outside-roost competition enhances fitness interdependence between frequent roostmates.

A combination of captive experiments and proximity sensing in the wild show that social bonds in vampire bats that are typically defined by cooperative interactions within the roost also extend beyond the roost and may provide benefits during foraging.     

The Brain’s Response to the Human Voice Depends on the Incidence of Autistic Traits in the General Population

Optimal brain sensitivity to the fundamental frequency (F0) contour changes in the human voice is important for understanding a speaker’s intonation, and consequently, the speaker’s attitude. However, whether sensitivity in the brain’s response to a human voice F0 contour change varies with an interaction between an individual’s traits (i.e., autistic traits) and a human voice element (i.e., presence or absence of communicative action such as calling) has not been investigated. In the present study, we investigated the neural processes involved in the perception of F0 contour changes in the Japanese monosyllables “ne” and “nu.” “Ne” is an interjection that means “hi” or “hey” in English; pronunciation of “ne” with a high falling F0 contour is used when the speaker wants to attract a listener’s attention (i.e., social intonation). Meanwhile, the Japanese concrete noun “nu” has no communicative meaning. We applied an adaptive spatial filtering method to the neuromagnetic time course recorded by whole-head magnetoencephalography (MEG) and estimated the spatiotemporal frequency dynamics of event-related cerebral oscillatory changes in beta band during the oddball paradigm. During the perception of the F0 contour change when “ne” was presented, there was event-related de-synchronization (ERD) in the right temporal lobe. In contrast, during the perception of the F0 contour change when “nu” was presented, ERD occurred in the left temporal lobe and in the bilateral occipital lobes. ERD that occurred during the social stimulus “ne” in the right hemisphere was significantly correlated with a greater number of autistic traits measured according to the Autism Spectrum Quotient (AQ), suggesting that the differences in human voice processing are associated with higher autistic traits, even in non-clinical subjects.