Summary of my answer. I like the Markov chain modeling but it misses the "temporal" aspect. On the other end, focusing on the temporal aspect (e.g. average time at $-1$) misses the "transition" aspect. I would go into the following general modelling (which with suitable assumption can lead to [markov process][1]). Also there is a lot of "censored" statistic behind this problem (which is certainly a classical problem of Software reliability ? ). The last equation of my answer gives the maximum likelihood estimator of voting intensity (up with "+" and dow with "-") for a given state of vote. As we can see from the equation, it is an intermediate from the case when you only estimate transition probability and the case when you only measure time spent at a given state. Hope this help.
General Modelling (to restate the question and assumptions).
Let $(VD_i)_{i\geq 1}$ and $(S_{i})_{i\geq 1}$ be random variables modelling respectively the voting dates and the associated vote sign (+1 for upvote, -1 for downvote). The voting process is simply
$$Y_{t}=Y^+_t-Y^-_t$$ where
$$Y^+_t=\sum_{i=0}^{\infty}1_{VD_i\leq t,S_i=1} \;\text{ and } \;Y^-_t=\sum_{i=0}^{\infty}1_{VD_i\leq t,S_i=-1}$$
The important quantity here is the intentity of $\epsilon$-jump
$$\lambda^{\epsilon}_t=\lim_{dt\rightarrow 0} \frac{1}{dt} P(Y^{\epsilon}_{t+dt}-Y^{\epsilon}_t=1|\mathcal{F}_t) $$
where $\epsilon$ can be $-$ or $+$ and $\mathcal{F}_t$ is a good filtration, in the genera case, without other knowledge it would be:
$$\mathcal{F}_t=\sigma \left (Y^+_t,Y^-_t,VD_1,\dots,VD_{Y^+_t+Y^-_t},S_{1},\dots,S_{Y^+_t+Y^-_t} \right )$$.
but along the lines of your question, I think you implicitly assume that
$$ P \left ( Y^{\epsilon}_{t+dt}-Y^{\epsilon}_t=1 | \mathcal{F}_t \right )= P \left (Y^{\epsilon}_{t+dt}-Y^{\epsilon}_t=1| Y_t \right ) $$
This means that for $\epsilon=+,-$ there exists a deterministic sequence $(\mu^{\epsilon}_i)_{i\in \mathbb{Z}}$ such that $\lambda^{\epsilon}_t=\mu^{\epsilon}_{Y_t}$.
Within this formalism, you question can be restated as: "it likely that $ \mu^{+}_{-1} -\mu^{+}_{0}>0$ " (or at least is the difference larger than a given threshold).
Under this assumption, it is easy to show that $Y_t$ is an [homogeneous markov process][3] on $\mathbb{Z}$ with generator $Q$ given by
$$\forall i,j \in \mathbb{Z}\;\;\; Q_{i,i+1}=\mu^{+}_{i}\;\; Q_{i,i-1}=\mu^{-}_{i}\;\; Q_{ii}=1-(\mu^{+}_{i}+\mu^{-}_{i}) \;\; Q_{ij}=0 \text{ if } |i-j|>1$$
Answering the question (through proposing a maximum likelihood estimatior for the statistical problem)
From this reformulation, solving the problem is done by estimating $(\mu^{+}_i)$ and building a test uppon its values. Let us fix and forget the $i$ index without loss of generality. Estimation of $\mu^+$ (and $\mu^-$) can be done uppon the observation of
$(T^{1},\eta^1),\dots,(T^{p},\eta^p)$ where $T^j$ are the lengths of the $j^{th}$ of the $p$ periods spent in state $i$ (i.e. successive times with $Y_t=i$) and $\eta^j$ is $+1$ if the question was upvoted, $-1$ if it was downvoted and $0$ if it was the last state of observation.
If you forget the case with the last state of observation, the mentionned couples are iid from a distribution that depends on $\mu_i^+$ and $\mu_i^-$: it is distributed as $(\min(Exp(\mu_i^+),Exp(\mu_i^-)),\eta)$ (where Exp is a random var from an exponential distribution and $\eta$ is + or -1 depending on who realizes the max).
Then, you can use the following simple lemma (the proof is straightforward):
Lemma If $X_+\leadsto Exp(\mu_+)$ and $X_{-} \leadsto Exp(\mu_{-})$ then, $T=\min(X_+,X_-)\leadsto Exp(\mu_++\mu_-)$ and $P(X_+1<X_-)=\frac{\mu_+}{\mu_++\mu_-}$.
This implies that the density $f(t,\epsilon)$ of $(T,\eta)$ is given by:
$$ f(t,\epsilon)=g_{\mu_++\mu_-}\left ( \frac{1(\epsilon=+1)*\mu_++1(\epsilon=-1)*\mu_-}{\mu_++\mu_-}\right )$$
where $g_a$ for $a>0$ is the density function of an exponential random variable with parameter $a$. From this expression, it is easy to derive the maximum likelihood estimator of $\mu_+$ and $\mu_-$:
$$(\hat{\mu}_+,\hat{\mu}_-)=argmin \ln (\mu_-+\mu_+)\left ( (\mu_-+\mu_+)\sum_{i=1}^p T^i+p\right )- p_-\ln\left (\mu_-\right ) -p_+ \ln \left (\mu_+\right )$$
where $p_-=|{i:\delta_i=-1}|$ and $p_+=|{i:\delta_i=+1}|$.
Comments for more advanced approaches
If you want to take into acount cases when $i$ is the last observed state (certainly smarter because when you go through $-1$, it is often your last score...) you have to modify a little bit the reasonning. The corresponding censoring is relatively classical...
Possible other approache may include the possibility of
- Having an intensity that decreases in with time
- Having an intensity that decreases with the time spent since the last vote (I prefer this one. In this case there are classical way of modelling how the density decreases...
- You may want to assume that $\mu_i^+$ is a smooth function of $i$
- .... you can propose other ideas !
