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Long-term memory of rating behaviors for the online trust formation

Xin-Yu Guo, Qiang Guo, Ren-De Li and Jian-Guo Liu

Physica A: Statistical Mechanics and its Applications, 2018, vol. 508, issue C, 254-264

Abstract: Investigating the dynamics of long-term memory in online rating behaviors is significant for understanding the evolution mechanism of collective behaviors and trust formation for online social networks. Since users are allowed to deliver ratings in many online systems, ratings can well reflect the user’s opinions. In this paper, we empirically investigate the long-term memory, measured by the Detrended Fluctuation Analysis, in collective rating behaviors before and after the trust formation. The results for the Epinions data set show that, comparing with the null model generated by the reshuffle process, the Hurst exponent of trustors (trustees) decreases 7.12% (9.05%) before and increases 7.36% (9.20%) after trust formation, which stably remains close to 0.5 in null model I and 0.6 in null model II, suggesting that the collective rating behavior plays an important role for the trust formation. Furthermore, we divide users into 8 groups according to the user degree and find that the correlation of the user degree and the variation of Hurst exponent, measured by the Pearson Correlation Coefficient, is 0.8629 and 0.8620 before and after trust formation respectively, reflecting a significant correlation between user degrees and collective rating behavior patterns. Finally, we select the users without creating other trust relations around the trust formation time and the result suggests that the collective rating behaviors indeed change for the trust formation. This work helps deeply understand the intrinsic feedback effects between collective behaviors and trust relationship.

Keywords: Collective behavior; Trust formation; DFA; Hurst exponent (search for similar items in EconPapers)
Date: 2018
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