SlowGuess/ACL_2020_OCR huggingface dataset:
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We filter the Reddit posts for known bots and spammers (Tan and Lee, 2015). We remove abbreviations, strings containing numbers, references to users and SRs, and both full and shortened hyperlinks. We convert British English spelling variants to American English and lemmatize all words. We follow Han and Baldwin (2011) in reducing repetitions of more than three letters (niiiiice) to three letters. Except for excluding stopwords, we do not employ a frequency threshold.
Model	sports	entertainment	knowledge	μ±σ
cfb	nba	nfl	gam	lol	mov	pol	sci	tec	AP	AUC
AP	AUC	AP	AUC	AP	AUC	AP	AUC	AP	AUC	AP	AUC	AP	AUC	AP	AUC	AP	AUC
JS	.632	.593	.617	.582	.626	.588	.619	.588	.609	.584	.622	.589	.614	.591	.649	.617	.638	.608	.625±.012	.593±.011
AA	.603	.556	.599	.556	.602	.553	.605	.561	.589	.553	.596	.552	.592	.556	.606	.558	.606	.562	.600±.006	.556±.003
CN	.600	.553	.596	.553	.598	.550	.602	.558	.585	.550	.592	.548	.588	.552	.601	.554	.603	.558	.596±.006	.553±.003
PA	.537	.517	.543	.527	.542	.522	.559	.545	.545	.534	.533	.519	.541	.534	.513	.503	.537	.526	.539±.011	.525±.011
Table 5: Performance on MWF prediction. The table shows AP and AUC of the models for the nine Subreddits as well as averaged scores. Grey highlighting illustrates the best score in a column, light grey the second-best.
A.2 Morphological Segmentation
We start by defining a set of potential stems  $O^{(i)}$  for each Subreddit  $i$ . A word  $w$  is given the status of a potential stem and added to  $O^{(i)}$  if it consists of at least 4 characters and has a frequency count of at least 100 in the Subreddit.
Then, to determine the stem of a specific word  $w$ , we employ an iterative algorithm. Let  $V^{(i)}$  be the vocabulary of the Subreddit, i.e., all words occurring in it. Define the set  $B_{1}$  of  $w$  as the bases in  $V^{(i)}$  that remain when one affix is removed, and that have a higher frequency count than  $w$  in the Subreddit. For example, reaction can be segmented as re\$action and react\$ion, so  $B_{1}(\text{reaction}) = \{\text{action}, \text{react}\}$  (assuming action and react both occur in the Subreddit and are more frequent than reaction). We then iteratively create  $B_{i+1}(w) = \bigcup_{b \in B_{i}(w)} B_{1}(b)$ . Let further  $B_{0}(w) = \{w\}$ . We define  $S(w) = O^{(i)} \cap B_{m}(w)$  with  $m = \max \{k | O^{(i)} \cap B_{k}(w) \neq \emptyset\}$  as the set of stems of  $w$ . If  $|S(w)| > 1$  (which is rarely the case in practice), the element with the lowest number of suffixes is chosen.
The algorithm is sensitive to most morpho-orthographic rules of English (Plag, 2003): when $ness is removed from happi$ness, e.g., the result is happy, not happi.
A.3 Message Passing Rule
Let  $\hat{\mathbf{B}}\in \mathbb{R}^{|\mathcal{V}|\times |\mathcal{V}|}$  be the adjacency matrix of the DG  $\mathcal{B}$  with added self-loops, i.e.,  $\hat{B}_{ii} = 1$  and  $\hat{\mathbf{D}}\in \mathbb{R}^{|\mathcal{V}|\times |\mathcal{V}|}$  the degree matrix of  $\hat{\mathbf{B}}$  with  $\hat{D}_{ii} = \sum_{j}\hat{B}_{ij}$ . The matrix form of the message passing step can be expressed as
$$
\mathbf {M} ^ {(l)} = \hat {\mathbf {D}} ^ {- \frac {1}{2}} \hat {\mathbf {B}} \hat {\mathbf {D}} ^ {- \frac {1}{2}} \mathbf {X} ^ {(l - 1)} \mathbf {W} ^ {(l)}, \tag {14}
$$
where  $\mathbf{W}^{(l)}$  is the trainable weight matrix of layer  $l$ , and  $\mathbf{X}^{(l-1)} \in \mathbb{R}^{|\mathcal{V}| \times |n|}$  is the matrix containing the node feature vectors from layer  $l-1$  (Kipf and Welling, 2016, 2017). The activation step then is
$$
\mathbf {X} ^ {(l)} = \operatorname {R e L U} \left(\mathbf {M} ^ {(l)}\right). \tag {15}
$$
A.4 Feature-based Link Prediction
Besides Jaccard similarity, we implement three other feature-based link prediction methods.
Adamic-Adar. The Adamic-Adar (AA) index (Adamic and Adar, 2003) has to take the bipartite structure of DGs into account. Using the modified definition of common neighbors as with  $\zeta_{JS}$ , we calculate it as
$$
\zeta_ {A A} (s, a) = \sum_ {\substack {n \in \Gamma_ {\cap} (s, a) \\ n \in \Gamma_ {\cap} (a, s)}} \frac {1}{d (n)}. \tag{16}
$$
Common Neighbors. The score of an edge  $(s, a)$  is calculated as the cardinality of the set of common neighbors (CN) of  $s$  and  $a$ . Similarly to  $\zeta_{JS}$  and  $\zeta_{AA}$ , we calculate the CN score as
$$
\zeta_ {C N} (s, a) = | \Gamma_ {\cap} (s, a) | + | \Gamma_ {\cap} (a, s) |. \tag {17}
$$
Preferential Attachment. For preferential attachment (PA), the score of an edge  $(s, a)$  is the product of the two node degrees,
$$
\zeta_ {P A} (s, a) = d (s) \cdot d (a). \tag {18}
$$
The training regime is identical to Jaccard similarity. AA outperforms PA and CN but is consistently beaten by JS (see Table 5).