Investigating the Impact of Multimodal Communication on Social Media Discourse: A Forensic Linguistic Analysis of Troll Speech and Mitigation Strategies

Project Abstract

This study examines how multimodal communication shapes social media discourse, with a forensic linguistic approach to identifying troll speech patterns and evaluating mitigation strategies. By integrating quantitative text analysis, visual rhetoric examination, and audio-visual cue coding, the research investigates how combinations of text, emojis, memes, images, videos, and vocal prosody influence perceived hostility, credibility, and behavioral intentions among online communities. The theoretical framework combines sociolinguistics, multimodal discourse analysis, and digital forensic methodology to trace how modality choices render messages more or less persuasive, inflammatory, or persuasive across cultures and platforms. A mixed-methods design employs corpus-based analyses of troll-laden threads from multiple platforms (e.g., Twitter/X, Facebook, Reddit, and image-based services) over a 12-month period, alongside controlled experimental stimuli where participants rate the aggressiveness, trustworthiness, and suggestibility of multimodal posts. Nearest-neighbor modeling and machine learning classifiers are used to detect modality-specific signatures of trolling, such as syntactic manipulation, sentiment polarity shift through memes, image-augmented sarcasm, and prosodic cues in short voice clips. The study also investigates the role of context, audience expectation, and platform affordances in mediating the impact of multimodal troll messages on topic salience, polarization, and user engagement metrics. In addition, it evaluates mitigation strategies, including real-time moderation algorithms, user education interventions, and design-based safeguards (content warnings, rapid rebuttal templates, and normative framing). Through scenario-based simulations, the effectiveness of these strategies is assessed in reducing spread, reducing perceived harm, and increasing resilience of online communities to manipulation. Findings are expected to reveal that multimodal cues amplify or dampen troll effectiveness depending on congruence between text, imagery, and sounds, as well as the prior stance of the audience. The research contributes to forensic linguistics by developing a modality-aware taxonomy of troll tactics, a cross-platform typology for rapid threat assessment, and a validated framework for measuring the ethical and legal implications of content moderation. Policy and platform-level recommendations emphasize transparent moderation criteria, accountability mechanisms for automated detectors, and culturally informed guidelines for multilingual multimodal trolling. The study anticipates significant implications for digital literacy programs, online dispute mediation, and the design of inclusive, less adversarial online ecosystems. By bridging theoretical models with empirical moderation outcomes, the research advances understanding of how multimodal communication shapes social media discourse and offers actionable strategies to mitigate the harms associated with troll activity while preserving legitimate expression.

Project Overview

What This Project Is About

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This project looks at how people communicate on social media using words, pictures, emojis, videos, and other cues. It asks how mixed modes of communication (text, images, sound) influence how trolls speak, how others respond, and what patterns show when speech becomes abusive or disruptive. It also explores ways to reduce harm by using linguistic analysis to guide moderation and education.

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The Problem It Addresses

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Trolls can distort online conversations and spread harmful messages. Traditional language analysis often misses the role of images, memes, and videos in shaping meaning. This project fills that gap by examining multiple signals together, helping platforms detect problematic behavior and suggesting practical ways to curb it without silencing legitimate voices.

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Objectives of the Project

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1. Identify common multimodal patterns used by trolls (text, images, GIFs, videos).
2. Explain how these patterns change the meaning of messages compared to text alone.
3. Assess how audiences react to multimodal troll posts on different platforms.
4. Develop guidelines for moderation that consider both language and visuals.
5. Propose educational resources to reduce harm and promote respectful online discourse.

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What You Will Do Step by Step

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Step 1: Review existing studies on online speech and multimodal communication.

Step 2: Collect a sample of trolling posts that combine text with images or videos.

Step 3: Code messages for language features and multimodal cues (tone, visual context).

Step 4: Analyze how combinations of cues change meaning and impact reactions.

Step 5: Compare patterns across platforms and communities.

Step 6: Consult literature on moderation and ethics to frame recommendations.

Step 7: Draft practical guidelines for platforms and users.

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Expected Outcome

We expect a clear map of how text plus images and other media interact to produce troll-like messages, plus practical moderation and education recommendations that can reduce harm while preserving free expression.