Happiness is assortative in online social networks

Social networks tend to disproportionally favor connections between individuals with either similar or dissimilar characteristics. This propensity, referred to as assortative mixing or homophily, is expressed as the correlation between attribute values of nearest neighbour vertices in a graph. Recent results indicate that beyond demographic features such as age, sex and race, even psychological states such as "loneliness" can be assortative in a social network. In spite of the increasing societal importance of online social networks it is unknown whether assortative mixing of psychological states takes place in situations where social ties are mediated solely by online networking services in the absence of physical contact. Here, we show that general happiness or Subjective Well-Being (SWB) of Twitter users, as measured from a 6 month record of their individual tweets, is indeed assortative across the Twitter social network. To our knowledge this is the first result that shows assortative mixing in online networks at the level of SWB. Our results imply that online social networks may be equally subject to the social mechanisms that cause assortative mixing in real social networks and that such assortative mixing takes place at the level of SWB. Given the increasing prevalence of online social networks, their propensity to connect users with similar levels of SWB may be an important instrument in better understanding how both positive and negative sentiments spread through online social ties. Future research may focus on how event-specific mood states can propagate and influence user behavior in "real life".Comment: 17 pages, 9 figure

NR2B receptor blockade inhibits pain-related sensitization of amygdala neurons

Pain-related sensitization and synaptic plasticity in the central nucleus of the amygdala (CeA) depend on the endogenous activation of NMDA receptors and phosphorylation of the NR1 subunit through a PKA-dependent mechanism. Functional NMDA receptors are heteromeric assemblies of NR1 with NR2A-D or NR3A, B subunits. NMDA receptors composed of NR1 and NR2B subunits have been implicated in neuroplasticity and are present in the CeA. Here we used a selective NR2B antagonist (Ro-256981) to determine the contribution of NR2B-containing NMDA receptors to pain-related sensitization of CeA neurons. Extracellular single-unit recordings were made from CeA neurons in anesthetized adult male rats before and during the development of an acute arthritis. Arthritis was induced in one knee joint by intraarticular injections of kaolin and carrageenan. Brief (15 s) mechanical stimuli of innocuous (100–500 g/30 mm2) and noxious (1000–2000 g/30 mm2) intensity were applied to the knee and other parts of the body. In agreement with our previous studies, all CeA neurons developed increased background and evoked activity after arthritis induction. Ro-256981 (1, 10 and 100 μM; 15 min each) was administered into the CeA by microdialysis 5–6 h postinduction of arthritis. Ro-256981 concentration-dependently decreased evoked responses, but not background activity. This pattern of effect is different from that of an NMDA receptor antagonist (AP5) in our previous studies. AP5 (100 μM – 5 mM) inhibited background activity and evoked responses. The differential effects of AP5 and Ro-256981 may suggest that NMDA receptors containing the NR2B subunit are important but not sole contributors to pain-related changes of CeA neurons

A DBN-Based Classifying Approach to Discover the Internet Water Army

Part 3: Web MiningInternational audienceThe Internet water army (IWA) usually refers to hidden paid posters and collusive spammers, which has already generated big threats for cyber security. Many researchers begin to study how to effectively identify the IWA. Currently, most efforts to distinguish non-IWA and IWA in data mining context focus on utilizing classification-based algorithms, including Bayesian Network, SVM, KNN and etc... However, Bayesian Network need strong conditional independence assumption, KNN has big computation costs, above approach may affect the effectiveness to some extent in real industrial applications. Hence, Neural Networks-like deep approach for IWA identification gradually becomes an emerging but possible direction and attempt. Unfortunately, there also exists one main problem, which is how to balance the deep learning and computation costs in hierarchical architecture. More specially, combine leaning-level heuristic training design and computing-level concurrent computation is a challenging issue. In this paper, we propose a collaborative hierarchical approach based on the deep belief network (DBN) for IWA identification. Firstly, a DBN-based collaborative model with hierarchical classifying mechanism is built. Then towards Hadoop platform, the Downpour Stochastic gradient descent (Downpour SGD) is exploited for DBN pre-training. Finally, the dynamical workflow will be designed for managing the whole learning-based classifying process. The experimental evaluation shows that the valid of our approach