Positive Spillover Effect in Attention Dynamics

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COLELLA, Sara, 2019. Positive Spillover Effect in Attention Dynamics [Dissertation]. Konstanz: University of Konstanz

@phdthesis{Colella2019Posit-48472, title={Positive Spillover Effect in Attention Dynamics}, year={2019}, author={Colella, Sara}, address={Konstanz}, school={Universität Konstanz} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/48472"> <dcterms:issued>2019</dcterms:issued> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/48472/3/Colella_2-ho1w66xsz7lj9.pdf"/> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nd/4.0/"/> <dcterms:abstract xml:lang="eng">Information available online is massive, but this is not the case of human attention. This difference can give rise to several dynamics, such as changing the attention allocation according to the occurring of offline events. Occurrence of additional topics of discussion may have several effects on the existing ones, both positive and negative. Researchers have studied deeply topic competition for the finite audience attention, but they have overlooked the positive effects that the diffusion of a topic might have on another topic. Because of this existing gap in literature we decided to focus on positive spillover effect: how topics might have a positive influence in the spread of other topics. To investigate attention dynamics, in this thesis considered as approximate measurement of the attention level on a topic its sharing behaviour: the number of tweets and retweets published on Twitter about the topic into consideration. Since posting about a particular subject increases its diffusion and visibility, online activity is a visual expression of attention. Different topics have different timelines and hence different shapes of the time series of the level of online activity. To reduce the variation level among the sharing dynamics, this thesis focused on only one class of topics: sudden celebrity deaths. In particular, how the attention level on celebrities varies after their sudden deaths. The level of attention vary according to the previous knowledge and interests about it. This is true for any topic, but even more for celebrities that base their own definition on people’s perceptions. Hence, our first result was to select a Twitter community with a common cultural background neglecting interaction and spatial proximity. Due to the lack of existing techniques in literature to select Twitter accounts that share the same “Little-c” culture, we developed a technique to select users whose individual cultural space intersects the one of the geographical area of interest. Once having implemented the developed technique to select the Italian Twitter territorial community, we analysed how the activity level on celebrities with an Italian audience varies over time. Our results show an initial state of equilibrium perturbed by an exogenous event: the announcement of the celebrity death. The system response is a sudden rise of activity, in agreement with the literature, and a power law attention decay during the relaxation phase after which the system is back to the initial state of equilibrium. Since most of the Twitter users tweet about each celebrity only once, the power law might be due to their aggregation. In any case, the decay rate is very similar, only slightly higher than what we would expect from the literature for this class of events. This thesis shows that the shape of the attention level rises and decays in the same way for most celebrities, but the height of main activity peak varies according to some characteristics about both the celebrities and their death. Our model offers a good prediction of the dimensions of the main activity peak according to the media coverage and news values of the topic, factors that foresee the newsworthiness of news according to the journalism literature. It is quite impressive that these last factors did not change substantially since the sixties and that they have an impact not only in reading, but also in sharing dynamics. Finally this thesis focus on the positive deviations of the foreseen attention level on the analysed celebrities. We considered these secondary peaks as possible evidences of the consequences of a positive spillover effect. We focused on the users responsible for the peaks, primary and secondaries, and on their participation in discussions regarding also other celebrities. According to overlap of the audience of the celebrities we were able to build a network in which the links among the celebrities represent the positive spillover effect. The analysis of the possible reasons behind the positive spillover effects was performed according to the temporal composition of each link. Our results show that the great majority of the links was due to topicality, similarities among the celebrities or their deaths, and only in few cases also the synchronicity of the deaths may have had a role. Furthermore, through the centrality analysis of the constructed topic network we were able to estimate which positive deviations of the foreseen attention level are due to the consequences of positive spillover effect among celebrities or instead to the noise of the system. For example, some secondary peaks are due to positive spillover effects triggered by other events, not necessarily related to other celebrities. The employed analysis of the interconnections among the topics, estimating the noise of the system, can be applied to several other types of system. Not isolated systems whose external influences cannot be precisely estimated. More in general, the performed network analysis show how the uncertainty level of the system (without forcing the data to agree to researchers’ expectations) is an information in itself. This approach is particularly useful if the complete knowledge of the factors that may have a role in the phenomenon under study is unknown, such as for attention dynamics.</dcterms:abstract> <dc:creator>Colella, Sara</dc:creator> <dc:rights>Attribution-NoDerivatives 4.0 International</dc:rights> <dc:contributor>Colella, Sara</dc:contributor> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:language>eng</dc:language> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/48472/3/Colella_2-ho1w66xsz7lj9.pdf"/> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-02-03T11:59:00Z</dcterms:available> <dcterms:title>Positive Spillover Effect in Attention Dynamics</dcterms:title> <bibo:uri rdf:resource="https://kops.uni-konstanz.de/handle/123456789/48472"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2020-02-03T11:59:00Z</dc:date> </rdf:Description> </rdf:RDF>

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