Influencing the Influencers: Evaluating Person-to-Person Influence on Social Networks Using Granger Causality

• URL: http://arxiv.org/abs/2110.04899v1
• Date: Sun, 10 Oct 2021 20:40:11 GMT
• Title: Influencing the Influencers: Evaluating Person-to-Person Influence on Social Networks Using Granger Causality
• Authors: Richard Kuzma, Iain J. Cruickshank, Kathleen M. Carley
• Abstract summary: We introduce a novel method for analyzing person-to-person content influence on Twitter. Using an Ego-Alter framework and Granger Causality, we examine President Donald Trump (the Ego) and the people he retweets (Alters) We find that each Alter has a different scope of influence across multiple topics, different magnitude of influence on a given topic, and the magnitude of a single Alter's influence can vary across topics.
• Score: 6.458496335718509
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce a novel method for analyzing person-to-person content influence on Twitter. Using an Ego-Alter framework and Granger Causality, we examine President Donald Trump (the Ego) and the people he retweets (Alters) as a case study. We find that each Alter has a different scope of influence across multiple topics, different magnitude of influence on a given topic, and the magnitude of a single Alter's influence can vary across topics. This work is novel in its focus on person-to-person influence and content-based influence. Its impact is two-fold: (1) identifying "canaries in the coal mine" who could be observed by misinformation researchers or platforms to identify misinformation narratives before super-influencers spread them to large audiences, and (2) enabling digital marketing targeted toward upstream Alters of super-influencers.

Related papers

• Social Convos: Capturing Agendas and Emotions on Social Media [1.6385815610837167]
  We present a novel approach to extract influence indicators from messages circulating among groups of users discussing particular topics. We focus on two influence indicators: the (control of) agenda and the use of emotional language.
  arXiv  Detail & Related papers  (2024-02-23T19:14:09Z)
• InfluencerRank: Discovering Effective Influencers via Graph Convolutional Attentive Recurrent Neural Networks [15.461845673443804]
  We propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time. Experiments have been conducted on an Instagram dataset that consists of 18,397 influencers with their 2,952,075 posts published within 12 months.
  arXiv  Detail & Related papers  (2023-04-04T15:48:08Z)
• What Drives Virtual Influencer's Impact? [0.0]
  This work examines how including someone else in photos shapes consumer responses to virtual influencers' posts. A multimethod investigation combines automated image and text analysis of thousands of social media posts. Companion presence makes virtual influencers seem more human, which makes them seem more trustworthy.
  arXiv  Detail & Related papers  (2023-01-24T09:22:41Z)
• Understanding How People Rate Their Conversations [73.17730062864314]
  We conduct a study to better understand how people rate their interactions with conversational agents. We focus on agreeableness and extraversion as variables that may explain variation in ratings.
  arXiv  Detail & Related papers  (2022-06-01T00:45:32Z)
• Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections [92.33485580547801]
  We analyze the change of polarization between the 2016 and 2020 U.S. presidential elections. Most of the top influencers were affiliated with media organizations during both elections. 75% of the top influencers in 2020 were not present in 2016, demonstrating that such status is difficult to retain.
  arXiv  Detail & Related papers  (2021-11-03T20:08:54Z)
• Analyzing Behavioral Changes of Twitter Users After Exposure to Misinformation [1.8251012479962594]
  We aim to understand whether general Twitter users changed their behavior after being exposed to misinformation. We compare the before and after behavior of exposed users to determine whether the frequency of the tweets they posted underwent any significant change. We also study the characteristics of two specific user groups, multi-exposure and extreme change groups, which were potentially highly impacted.
  arXiv  Detail & Related papers  (2021-11-01T04:48:07Z)
• An influencer-based approach to understanding radical right viral tweets [0.0]
  ROT provides insight into the content, engagement and followership of a set of 35 radical right influencers. It includes over 50,000 original entries and over 40 million retweets, quotes, replies and mentions. It is crucial to account for the influencer-level structure, and find evidence of the importance of both influencer- and content-level factors.
  arXiv  Detail & Related papers  (2021-09-15T21:40:25Z)
• Machine learning reveals how personalized climate communication can both succeed and backfire [55.41644538483948]
  We show that online advertisements increase some people's belief in climate change while resulting in decreased belief in others. In particular, we show that the effect of the advertisements could change depending on people's age and ethnicity.
  arXiv  Detail & Related papers  (2021-09-10T20:47:34Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
  We argue that it is critical to understand what user attributes potentially cause users to share fake news. In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. We propose a principled approach to alleviating selection bias in fake news dissemination.
  arXiv  Detail & Related papers  (2020-10-20T19:37:04Z)
• Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
  We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
  arXiv  Detail & Related papers  (2020-04-20T20:00:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.