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Article
Publication date: 4 October 2021

Quentin Grossetti, Cedric du Mouza, Nicolas Travers and Camelia Constantin

Social network platforms are considered today as a major communication mean. Their success leads to an unprecedented growth of user-generated content; therefore, finding…

Abstract

Purpose

Social network platforms are considered today as a major communication mean. Their success leads to an unprecedented growth of user-generated content; therefore, finding interesting content for a given user has become a major issue. Recommender systems allow these platforms to personalize individual experience and increase user engagement by filtering messages according to user interest and/or neighborhood. Recent research results show, however, that this content personalization might increase the echo chamber effect and create filter bubbles that restrain the diversity of opinions regarding the recommended content.

Design/methodology/approach

The purpose of this paper is to present a thorough study of communities on a large Twitter data set that quantifies the effect of recommender systems on users’ behavior by creating filter bubbles. The authors further propose their community-aware model (CAM) that counters the impact of different recommender systems on information consumption.

Findings

The authors propose their CAM that counters the impact of different recommender systems on information consumption. The study results show that filter bubbles effects concern up to 10% of users and the proposed model based on the similarities between communities enhance recommendations.

Originality/value

The authors proposed the CAM approach, which relies on similarities between communities to re-rank lists of recommendations to weaken the filter bubble effect for these users.

Details

International Journal of Web Information Systems, vol. 17 no. 6
Type: Research Article
ISSN: 1744-0084

Keywords

Article
Publication date: 12 August 2014

Nicolas Travers, Zeinab Hmedeh, Nelly Vouzoukidou, Cedric du Mouza, Vassilis Christophides and Michel Scholl

The purpose of this paper is to present a thorough analysis of three complementary features of real-scale really simple syndication (RSS)/Atom feeds, namely, publication activity…

Abstract

Purpose

The purpose of this paper is to present a thorough analysis of three complementary features of real-scale really simple syndication (RSS)/Atom feeds, namely, publication activity, items characteristics and their textual vocabulary, that the authors believe are crucial for emerging Web 2.0 applications. Previous works on RSS/Atom statistical characteristics do not provide a precise and updated characterization of feeds’ behavior and content, characterization that can be used to successfully benchmark the effectiveness and efficiency of various Web syndication processing/analysis techniques.

Design/methodology/approach

The authors empirical study relies on a large-scale testbed acquired over an eight-month campaign from 2010. They collected a total number of 10,794,285 items originating from 8,155 productive feeds. The authors deeply analyze feeds productivity (types and bandwidth), content (XML, text and duplicates) and textual content (vocabulary and buzz-words).

Findings

The findings of the study are as follows: 17 per cent of feeds produce 97 per cent of the items; a formal characterization of feeds publication rate conducted by using a modified power law; most popular textual elements are the title and description, with the average size of 52 terms; cumulative item size follows a lognormal distribution, varying greatly with feeds type; 47 per cent of the feed-published items share the same description; the vocabulary does not belong to Wordnet terms (4 per cent); characterization of vocabulary growth using Heaps’ laws and the number of occurrences by a stretched exponential distribution conducted; and ranking of terms does not significantly vary for frequent terms.

Research limitations/implications

Modeling dedicated Web applications capacities, Defining benchmarks, optimizing Publish/Subscribe index structures.

Practical implications

It especially opens many possibilities for tuning Web applications, like an RSS crawler designed with a resource allocator and a refreshing strategy based on the Gini values and evolution to predict bursts for each feed, according to their category and class for targeted feeds; an indexing structure which matches textual items’ content, which takes into account item size according to targeted feeds, size of the vocabulary and term occurrences, updates of the vocabulary and evolution of term ranks, typos and misspelling correction; filtering by pruning items for content duplicates of different feeds and correlation of terms to easily detect replicates.

Originality/value

A content-oriented analysis of dynamic Web information.

Details

International Journal of Web Information Systems, vol. 10 no. 3
Type: Research Article
ISSN: 1744-0084

Keywords

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