Partisan bot-like accounts continue to amplify divisive content on X
Partisan, bot-like accounts on X (formerly Twitter) are allegedly generating billions of impressions by amplifying divisive and often misleading political content, with some commenters linking this to recent real-world unrest such as UK riots. Participants debate whether Elon Musk’s ownership and algorithmic choices have worsened an existing problem or simply exposed more viewpoints, and whether corporate or government moderation can curb abuse without undermining free speech. Alternatives like Threads, Mastodon, and heavy user-side filtering are discussed, but many see large, engagement-driven platforms as structurally prone to manipulation and polarization.
State of X and Bots
- Many see X as overrun by bots and divisive political content, likened to a nightclub of robots or old practices like paid crowds and claqueurs—“all that’s old is new again.”
- Some say this isn’t new; Twitter was already bad pre-acquisition and part of a broader trend of AI-enabled manipulation and election cycles.
Role of Musk and Platform Direction
- One camp argues Musk drastically worsened things: “drove the tanker truck of gasoline,” fired moderation “firefighters,” turned X into a larger Gab-like right‑wing echo chamber, and uses it as a personal political megaphone.
- Others argue he better grasps Twitter’s value as a messy but uniquely mixed public square, preferable to more sanitized, algorithmically soothing platforms.
- Some think X was always a celebrity/marketing site and its current trajectory was inevitable.
Free Speech, Harm, and Algorithms
- Debate over whether sexist/racist tropes and “off‑color” jokes should be allowed: some emphasize harm, “punching down,” and historical links between dehumanizing rhetoric and fascist violence (invoking the “Paradox of Tolerance”).
- Others counter that using this to justify speech controls is self‑contradictory and that many “divisive” comments are simply observations that elites say must not be voiced.
- Several distinguish between organic virality and algorithmic boosting of fringe accounts; some argue algorithmic engagement maximization inherently amplifies outrage.
Authenticity, Bot Detection, and Business Incentives
- Some note the report’s “bot‑like” wording and absence of a public account list, arguing this makes it hard to verify and suspecting conflation of anti‑progressive speech with disinformation.
- Others say bots pretending to be people are inherently bad regardless of viewpoint.
- Claims that large botnets should be trivially catchable meet counters that filters exist but are effectively disabled to inflate engagement metrics.
- There’s discussion of API paywalls, continued free automation allowances, and rampant spam on trending tags.
UK Riots and National Security
- Several link X’s misinformation ecosystem to recent UK race riots and frame this as a national security issue.
- Others argue the unrest reflects deeper immigration and governance grievances and broader social‑media dynamics, not just X.
- One detailed reply challenges this narrative, attributing riots to far‑right misinformation about a specific attack and disputing statistics about arrests and protester behavior, calling some claims misleading.
Alternatives and User Coping Strategies
- Some users report retreating to Threads, Mastodon, or curated tools; opinions on Threads range from “sweet spot” to “cesspool” similar to X.
- Power users describe heavy filtering: browser extensions, whitelists, muted words, and manual digests to strip out bots, ads, and algorithmic suggestions.
- Others argue that even if you clean your own feed, you can’t escape the real‑world impact of those influenced by the platform’s “dross.”
Government Influence and Bias Debates
- One side distrusts pre‑Musk Twitter for alleged government‑driven takedowns and liberal algorithmic bias, seeing current X as freer despite flaws.
- Another cites reports that X now complies more with government censorship requests and has reduced transparency about them.
- Disagreement persists over the scale and political lean of pre‑Musk bots; some claim mainly right‑wing botnets, others insist earlier bots were on “the other side.” The true distribution is left unclear in the thread.