NUNI (New User and New Item) Problem for SRSs Using Content Aware Multimedia-Based Approach

The processing of pre-experimentally unfamiliar stimuli such as abstract figures and non-words is poorly understood. Here, we considered the role of memory strength in the discrimination process of such stimuli using a three-phase old/new recognition memory paradigm. Memory strength was manipulated as a function of the levels of processing (deep vs. shallow) and repetition. Behavioral results were matched to brain responses using EEG. We found that correct identification of the new abstract figures and non-words was superior to old item recognition when they were merely studied without repetition, but not when they were semantically processed or drawn. EEG results indicated that successful new item identification was marked by a combination of the absence of familiarity (N400) and recollection (P600) for the studied figures. For both the abstract figures and the non-words, the parietal P600 was found to differentiate between the old and new items (late old/new effects). The present study extends current knowledge on the processing of pre-experimentally unfamiliar figurative and verbal stimuli by showing that their discrimination depends on experimentally induced memory strength and that the underlying brain processes differ. Nevertheless, the P600, similar to pre-experimentally familiar figures and words, likely reflects improved recognition memory of meaningless pictorial and verbal items.

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Echo chamber detection and analysis

Social Network Analysis and Mining ◽

10.1007/s13278-021-00779-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Giacomo Villa ◽

Gabriella Pasi ◽

Marco Viviani

Keyword(s):

Social Media ◽

Community Detection ◽

Virtual Communities ◽

Selective Exposure ◽

Social Needs ◽

News Sources ◽

Detection Strategy ◽

Echo Chambers ◽

Echo Chamber ◽

Content Aware

AbstractSocial media allow to fulfill perceived social needs such as connecting with friends or other individuals with similar interests into virtual communities; they have also become essential as news sources, microblogging platforms, in particular, in a variety of contexts including that of health. However, due to the homophily property and selective exposure to information, social media have the tendency to create distinct groups of individuals whose ideas are highly polarized around certain topics. In these groups, a.k.a. echo chambers, people only "hear their own voice,” and divergent visions are no longer taken into account. This article focuses on the study of the echo chamber phenomenon in the context of the COVID-19 pandemic, by considering both the relationships connecting individuals and semantic aspects related to the content they share over Twitter. To this aim, we propose an approach based on the application of a community detection strategy to distinct topology- and content-aware representations of the COVID-19 conversation graph. Then, we assess and analyze the controversy and homogeneity among the different polarized groups obtained. The evaluations of the approach are carried out on a dataset of tweets related to COVID-19 collected between January and March 2020.

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