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Social listening

Available on growth, enterprise

What people say about your brand on social, read the way an AI engine reads it — what is at risk, what is working, and who carries the most weight.

#In 10 seconds

• This is not a posting tool. It reads what other people wrote about you and tells you what it means for how AI answers about your brand.
• Twitter / X is the platform with real data today. LinkedIn has its own view. Reddit, Instagram and TikTok are marked Soon and show a placeholder.
• It is a Growth feature. On Starter you see a lock rather than an empty page.

#Before you start

• Your brand must be on Growth or higher. On Starter and on the free plan this page shows a lock. See Plan and usage.
• Your first collection must have finished. Until then you get "We're still preparing this view" — that is timing, not a restriction.
• Nothing to connect. EOMA collects public posts itself; you do not link a social account for this — and it does not publish to social accounts at all, so there is no account to link for that either.

#Steps

#1 — Pick a platform in the sidebar

┌─ sidebar ────────────────────────────┐
│  ▼ Social Listening                  │
│      ● Twitter / X        ← here     │
│        LinkedIn                      │
│        Reddit          [Soon]        │
│        Instagram       [Soon]        │
│        TikTok          [Soon]        │
└──────────────────────────────────────┘

The three with a Soon badge open a placeholder card, not data. Clicking the section header alone lands you on Twitter / X.

#2 — Stay on Topics unless you want the raw stream

┌──────────────────────┐
│ [ Topics ] [ Feed ]  │  ← Topics is the analysis
└──────────────────────┘

Topics is the read: sentiment, risk, who is loudest, what changed. Feed is the unprocessed timeline. The toggle does not appear on LinkedIn or on a Soon platform.

#3 — Click anything to see the posts behind it

A donut segment, a topic, a voice, a point on the trend — each one opens a panel listing the actual posts that produced the number. Nothing here is a figure you have to take on trust.

#What you'll see

#The heading — "What people say about your brand"

A short intro band across the top, then an explainer card you can dismiss.

#The section title — "How AI sees you on Twitter"

With a line explaining the premise: what people say about you in public is the raw material AI engines draw on when they answer questions about your brand.

#The sentiment donut and the three figures beside it

A ring split into Positive, Neutral and Negative with the negative share called out in the middle, and to its right: AI-risk score, Negative reach and Classified mentions.

#"At-risk conversations" and "Positive signals"

Side by side. The left lists negative posts about you, most serious first, each with a severity tag and a confidence figure. The right lists favourable posts ranked by how far they reached.

#"Topics by sentiment" and "Top voices"

Side by side under those. Topics ranks the subjects people raise by how negative the conversation is. Top voices ranks the accounts by audience size, with how each one feels about you.

#"Sentiment over time"

A full-width chart at the bottom, weekly volume by sentiment.

#Reading the numbers

AI-risk score — for example 34 / 100. Of all the audience that has heard something about you on this platform, 34% of that reach was negative. It is weighted by follower count on purpose: one complaint from an account with 200,000 followers counts for more than fifty from accounts with 40, because that is the weighting an AI model effectively applies too. The colour moves with the number — green under 30, amber to 60, red above.

Negative reach — for example 84.2K, "vs 12.1K positive · louder than positive". The critical posts about you reached about 84,200 people; the favourable ones reached 12,100. When negative is louder, the version of your story with the widest circulation is the unflattering one, and that is the version an AI model is most likely to have absorbed. The fix is rarely to argue with the critics — it is to give the favourable side more reach.

Classified mentions — for example 612. EOMA found and read 612 posts naming your brand in this window. This is the sample size behind everything else on the page: at 612 the topic breakdown is worth acting on; at 12 it is an anecdote.

"38% neg · 44" on a topic row. Forty-four posts raised this subject and 38% of them were negative. Topics are sorted worst-first, so the row at the top is the angle most likely to surface unfavourably when someone asks AI about you. Its position matters more than its exact percentage.

"120K followers" on a top voice. That account reaches roughly 120,000 people when it mentions you. A single negative voice at that size outweighs a long tail of small positive ones in what AI ends up learning — which is why this list is ranked by reach and not by how often someone posts.

"confidence 87%" on an at-risk post. How sure the classifier is that this post is negative about you specifically. Anything low is worth opening before acting on — sarcasm and brand names that double as ordinary words are where it slips.

A spike in the weekly chart. One bad week is noise. Several negative weeks in a row is what actually reshapes how AI describes you, because models learn from the accumulated conversation rather than from any single post.

#When it goes wrong

#Related

• AI provider visibility — what AI actually says, once it has read all this
• Plan and usage — which plan includes social listening
• Actions — where a negative topic becomes work EOMA can do