Distilling Information Reliability and Source Trustworthiness from Digital Traces

Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, we argue that the temporal traces left by these noisy evaluations give cues on the reliability of the information and the trustworthiness of the sources. Then, we propose a temporal point process modeling framework that links these temporal traces to robust, unbiased and interpretable notions of information reliability and source trustworthiness. Furthermore, we develop an efficient convex optimization procedure to learn the parameters of the model from historical traces. Experiments on real-world data gathered from Wikipedia and Stack Overflow show that our modeling framework accurately predicts evaluation events, provides an interpretable measure of information reliability and source trustworthiness, and yields interesting insights about real-world events.Comment: Accepted at 26th World Wide Web conference (WWW-17

Solvent effect on protonation of tpps in water-DMF mixtures

The protonation of 5,10,15,20-tetrakis(4-sulfonatophenyl)porphyrin was investigated in aqueous solutions of N,N-dimethyformamide at 25 °C and 0.1 mol.dm-3 sodium perchlorate. The solvent effect on value of protonation constant was examined by using the linear solvation energy relationship concept. The value of logK1,logK2 and logKt was correlated with the macroscopic (dielectric constant) and microscopic Kamlet-Taft parameters (a, b and p*) of binary mixtures. The solvent effects were analyzed in the terms of Kamlet, Abboud and Taft model (KAT). Multiple linear regression were used to find the contribution of the microscopic parameters containing a (hydrogen-bond acidity), p* (dipolarity/polarizability) and b (hydrogen-bond basicity). It was found that a and b were the most predominant descriptors. Also, relationship with reciprocal of dielectric constant was obtained based on Born’s model, showing the significance of specific solute-solvent interactions. Therefore the hydrogen bonding interactions between solute and solvent components are mainly responsible for the change in protonation constants of 5,10,15,20-tetrakis(4-sulfonatophenyl)porphyrin in water- N,N-dimethyformamid binary mixtures. KEY WORDS: Protonation, TPPS, Solvent effects, Aqueous mixture, DMF Bull. Chem. Soc. Ethiop. 2016, 30(3), 457-464DOI: http://dx.doi.org/10.4314/bcse.v30i3.1

Correlated Cascades: Compete or Cooperate

In real world social networks, there are multiple cascades which are rarely independent. They usually compete or cooperate with each other. Motivated by the reinforcement theory in sociology we leverage the fact that adoption of a user to any behavior is modeled by the aggregation of behaviors of its neighbors. We use a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks. The resulting inference problem is proved to be convex and is solved in parallel by using the barrier method. The advantage of the proposed model is twofold; it models correlated cascades and also learns the latent diffusion network. Experimental results on synthetic and two real datasets gathered from Twitter, URL shortening and music streaming services, illustrate the superior performance of the proposed model over the alternatives