Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications

Ben Dennes; Hywel T.P. Williams (University of Exeter); Saffron O'Neill; Travis Coan; Tristan J.B. Cann

Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications

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arxiv 2303.16694 v1 pith:QW6TGMI6 submitted 2023-03-29 cs.SI cs.CL

Using Semantic Similarity and Text Embedding to Measure the Social Media Echo of Strategic Communications

Tristan J.B. Cann , Ben Dennes , Travis Coan , Saffron O'Neill , Hywel T.P. Williams (University of Exeter) This is my paper

classification cs.SI cs.CL

keywords onlinechangecommunicationscontentdiscourseimpactmediamessage

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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Online discourse covers a wide range of topics and many actors tailor their content to impact online discussions through carefully crafted messages and targeted campaigns. Yet the scale and diversity of online media content make it difficult to evaluate the impact of a particular message. In this paper, we present a new technique that leverages semantic similarity to quantify the change in the discussion after a particular message has been published. We use a set of press releases from environmental organisations and tweets from the climate change debate to show that our novel approach reveals a heavy-tailed distribution of response in online discourse to strategic communications.

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