Revisiting Information Cascades in Online Social Networks
It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/1/77 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841549143586111488 |
|---|---|
| author | Michael Sidorov Ofer Hadar Dan Vilenchik |
| author_facet | Michael Sidorov Ofer Hadar Dan Vilenchik |
| author_sort | Michael Sidorov |
| collection | DOAJ |
| description | It is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting macro-level features, such as the total cascade size, relatively little attention has been given to the prediction of micro-level features, such as the activity of an individual user. In this study we aim to address this gap by proposing a method to predict the activity of individual users in an OSN, relying solely on their interactions rather than prior knowledge of their social network. We evaluated our results on four large datasets, each comprising over 14 million tweets, recorded on <b>X</b> social network across four different topics over several month. Our method achieved a mean <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula> score of 0.86, with a best result of 0.983. |
| format | Article |
| id | doaj-art-63cb9f69e8274af19c0a39151dd85521 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-63cb9f69e8274af19c0a39151dd855212025-01-10T13:18:10ZengMDPI AGMathematics2227-73902024-12-011317710.3390/math13010077Revisiting Information Cascades in Online Social NetworksMichael Sidorov0Ofer Hadar1Dan Vilenchik2School of Electrical and Computer Engineering, Ben Gurion University of the Negev, Be’er Sheba 84105001, IsraelSchool of Electrical and Computer Engineering, Ben Gurion University of the Negev, Be’er Sheba 84105001, IsraelSchool of Electrical and Computer Engineering, Ben Gurion University of the Negev, Be’er Sheba 84105001, IsraelIt is widely believed that a user’s activity pattern in Online Social Networks (OSNs) is strongly influenced by their friends or the users they follow. Building on this intuition, numerous models have been proposed over the years to predict information propagation in OSNs. Many of these models drew inspiration from the process of infectious spread within a population. While this approach is definitely plausible, it relies on knowledge of users’ social connections, which can be challenging to obtain due to privacy concerns. Moreover, while a significant body of work has focused on predicting macro-level features, such as the total cascade size, relatively little attention has been given to the prediction of micro-level features, such as the activity of an individual user. In this study we aim to address this gap by proposing a method to predict the activity of individual users in an OSN, relying solely on their interactions rather than prior knowledge of their social network. We evaluated our results on four large datasets, each comprising over 14 million tweets, recorded on <b>X</b> social network across four different topics over several month. Our method achieved a mean <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>F</mi><mn>1</mn></msub></semantics></math></inline-formula> score of 0.86, with a best result of 0.983.https://www.mdpi.com/2227-7390/13/1/77deep learningonline social networksinformation cascadesmachine learninginformation diffusion models |
| spellingShingle | Michael Sidorov Ofer Hadar Dan Vilenchik Revisiting Information Cascades in Online Social Networks Mathematics deep learning online social networks information cascades machine learning information diffusion models |
| title | Revisiting Information Cascades in Online Social Networks |
| title_full | Revisiting Information Cascades in Online Social Networks |
| title_fullStr | Revisiting Information Cascades in Online Social Networks |
| title_full_unstemmed | Revisiting Information Cascades in Online Social Networks |
| title_short | Revisiting Information Cascades in Online Social Networks |
| title_sort | revisiting information cascades in online social networks |
| topic | deep learning online social networks information cascades machine learning information diffusion models |
| url | https://www.mdpi.com/2227-7390/13/1/77 |
| work_keys_str_mv | AT michaelsidorov revisitinginformationcascadesinonlinesocialnetworks AT oferhadar revisitinginformationcascadesinonlinesocialnetworks AT danvilenchik revisitinginformationcascadesinonlinesocialnetworks |