A missing value approach to social network data: “Dislike” or “Nothing”?

Mariani, Paolo; Marletta, Andrea; Mussini, Mauro; Zenga, M; Grammatica, E

doi:10.1007/s10287-020-00381-6

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…A possible solution could be represented by comparing the distribution of "Likes" and its complementary. The hypothesis is that the behaviour of the complementary observations is similar to those of real observations (Mariani et al 2020). Alternatively, the absence of a "Like" can be measured on the basis of a placed "Like" for the same user for a similar social page (Mariani et al 2019).…”

Section: Introductionmentioning

confidence: 98%

Missing values and data enrichment: an application to social media liking

2022

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In the big data context, it is very frequent to manage the analysis of missing values. This is especially relevant in the field of statistical analysis, where this represents a thorny issue. This study proposes a strategy for data enrichment in presence of sparse matrices. The research objective consists in the evaluation of a possible distinction of behaviour among observations in sparse matrices with missing data. After selecting among the multiple imputation methods, an innovative technique will be presented to impute missing observations as a negative position or a neutral opinion. This method has been applied to a dataset measuring the interaction between users and social network pages for some Italian newspapers.

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Section: Introductionmentioning

confidence: 98%

Missing values and data enrichment: an application to social media liking

2022

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“…Link prediction and imputation are among the most common techniques for addressing missing data in OSNs. Nevertheless, the e cacy of these methods may be constrained due to their heavy dependence on the interactions or connections between nodes for the estimation of missing data (Alam et al, 2023;Aziz et al, 2023;Mariani et al, 2020). Another widely adopted approach entails the creation of synthetic social networks if the real-world network is unavailable.…”

Section: Introductionmentioning

confidence: 99%

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Hasan,

Bakar,

Yaakub

2024

Preprint

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Social network analysis is a powerful tool for understanding various phenomena, but it requires data with explicit connections among users. However, such data is hard to obtain in real-time, especially from platforms like X, commonly known as Twitter, where users share topic-related content rather than personal connections. Therefore, this paper tackles a new problem of building a social network graph in real-time where explicit connections are unavailable. Our methodology is centred around the concept of user similarity as the fundamental basis for establishing connections, suggesting that users with similar characteristics are more likely to form connections. To implement this concept, we extracted easily accessible attributes from the Twitter platform and proposed a novel graph model based on similarity. We also introduce an Attribute-Weighted Euclidean Distance (AWED) to calculate user similarities. We compare the proposed graph with synthetic graphs based on network properties, online social network characteristics, and predictive analysis. The results suggest that the AWED graph provides a more precise representation of the dynamic connections that exist in real-world online social networks, surpassing the inherent constraints of synthetic graphs. We demonstrate that the proposed method of graph construction is simple, flexible, and effective for network analysis tasks.

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A missing value approach to social network data: “Dislike” or “Nothing”?

Cited by 2 publications

References 18 publications

Missing values and data enrichment: an application to social media liking

Missing values and data enrichment: an application to social media liking

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

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