  Political activity on social media presents a data-rich window into political
behavior, but the vast amount of data means that almost all content analyses of
social media require a data labeling step. However, most automated machine
classification methods ignore the multimodality of posted content, focusing
either on text or images. State-of-the-art vision-and-language models are
unusable for most political science research: they require all observations to
have both image and text and require computationally expensive pretraining.
This paper proposes a novel vision-and-language framework called multimodal
representations using modality translation (MARMOT). MARMOT presents two
methodological contributions: it can construct representations for observations
missing image or text, and it replaces the computationally expensive
pretraining with modality translation. MARMOT outperforms an ensemble text-only
classifier in 19 of 20 categories in multilabel classifications of tweets
reporting election incidents during the 2016 U.S. general election. Moreover,
MARMOT shows significant improvements over the results of benchmark multimodal
models on the Hateful Memes dataset, improving the best result set by
VisualBERT in terms of accuracy from 0.6473 to 0.6760 and area under the
receiver operating characteristic curve (AUC) from 0.7141 to 0.7530.
